align.py

from config import *
from wordSim import *
from util import *




##############################################################################################################################
def alignNouns(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]
   
    global ppdbSim
    global theta1
    print(source)
    print(target)
    existingAlignments=[]
    nounAlignments = []
    sourceWordIndicesAlreadyAligned=sorted(list(set([item[0] for item in existingAlignments])))
    targetWordIndicesAlreadyAligned=sorted(list(set([item[1] for item in existingAlignments])))
    sourceWordIndices = [i+1 for i in range(len(source))]
    targetWordIndices = [i+1 for i in range(len(target))]

    #print(source)
    #print(target)
    sourceWords = [item[0] for item in source]
    targetWords = [item[0] for item in target]

    sourceLemmas = [item[0] for item in source]
    targetLemmas = [item[0] for item in target]

    sourcePosTags = [item[0] for item in source]
    targetPosTags = [item[0] for item in target]

    

    numberOfNounsInSource = 0

    evidenceCountsMatrix = {}
    relativeAlignmentsMatrix = {}
    wordSimilarities = {}
    #sourceWordIndicesAlreadyAligned=[]
    #print(sourceWordIndices)
    # construct the two matrices in the following loop
    for i in sourceWordIndices:
        #print(i)
        print(sourcePosTags[i-1][0])
        if i in (sourcePosTags[i-1][0].lower() != 'n' and sourcePosTags[i-1].lower() != 'prp'):
            continue

        numberOfNounsInSource += 1

        for j in targetWordIndices:
            if j in (targetPosTags[j-1][0].lower() != 'n' and targetPosTags[j-1].lower() != 'prp'):
                continue


            if max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))<ppdbSim:
                continue


            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))


            sourceWordParents = findParents(sourceDParse, i, sourceWords[i-1])
            sourceWordChildren = findChildren(sourceDParse, i, sourceWords[i-1])
            targetWordParents = findParents(targetDParse, j, targetWords[j-1])
            targetWordChildren = findChildren(targetDParse, j, targetWords[j-1])


            # search for common or equivalent parents
            groupOfSimilarRelationsForNounParent = ['pos', 'nn', 'prep_of', 'prep_in', 'prep_at', 'prep_for']
            group1OfSimilarRelationsForVerbParent = ['agent', 'nsubj', 'xsubj']
            group2OfSimilarRelationsForVerbParent = ['ccomp', 'dobj', 'nsubjpass', 'rel', 'partmod']
            group3OfSimilarRelationsForVerbParent = ['tmod' 'prep_in', 'prep_at', 'prep_on']
            group4OfSimilarRelationsForVerbParent = ['iobj', 'prep_to']


                        
            for ktem in sourceWordParents:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in nounAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                        (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsForNounParent and ltem[2] in groupOfSimilarRelationsForNounParent) or
                            (ktem[2] in group1OfSimilarRelationsForVerbParent and ltem[2] in group1OfSimilarRelationsForVerbParent) or
                            (ktem[2] in group2OfSimilarRelationsForVerbParent and ltem[2] in group2OfSimilarRelationsForVerbParent) or
                            (ktem[2] in group3OfSimilarRelationsForVerbParent and ltem[2] in group3OfSimilarRelationsForVerbParent) or
                            (ktem[2] in group4OfSimilarRelationsForVerbParent and ltem[2] in group4OfSimilarRelationsForVerbParent)):
                        
                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for common or equivalent children
            groupOfSimilarRelationsForNounChild = ['pos', 'nn' 'prep_of', 'prep_in', 'prep_at', 'prep_for']
            groupOfSimilarRelationsForVerbChild = ['infmod', 'partmod', 'rcmod']
            groupOfSimilarRelationsForAdjectiveChild = ['amod', 'rcmod']


            for ktem in sourceWordChildren:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in nounAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsForNounChild and ltem[2] in groupOfSimilarRelationsForNounChild) or
                            (ktem[2] in groupOfSimilarRelationsForVerbChild and ltem[2] in groupOfSimilarRelationsForVerbChild) or
                            (ktem[2] in groupOfSimilarRelationsForAdjectiveChild and ltem[2] in groupOfSimilarRelationsForAdjectiveChild)):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for equivalent parent-child relations
            groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild = [['nsubj'], ['amod', 'rcmod']]
            groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['ccomp', 'dobj', 'nsubjpass', 'rel', 'partmod'], ['infmod', 'partmod', 'rcmod']]
            group1OfSimilarRelationsInOppositeDirectionForNounParentAndChild = [['conj_and'], ['conj_and']]
            group2OfSimilarRelationsInOppositeDirectionForNounParentAndChild = [['conj_or'], ['conj_or']]
            group3OfSimilarRelationsInOppositeDirectionForNounParentAndChild = [['conj_nor'], ['conj_nor']]


            for ktem in sourceWordParents:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in nounAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for equivalent child-parent relations
            for ktem in sourceWordChildren:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in nounAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForNounParentAndChild[1] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForNounParentAndChild[0])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])





    # now use the collected stats to align
    for n in range(numberOfNounsInSource):

        maxEvidenceCountForCurrentPass = 0
        maxOverallValueForCurrentPass = 0
        indexPairWithStrongestTieForCurrentPass = [-1, -1]


        for i in sourceWordIndices:
            if i in sourcePosTags[i-1][0].lower() != 'n' or sourceLemmas[i-1] in stop_words:
                continue

            for j in targetWordIndices:
                if j in targetPosTags[j-1][0].lower() != 'n' or targetLemmas[j-1] in stop_words:
                    continue

                if (i, j) in evidenceCountsMatrix and theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]>maxOverallValueForCurrentPass:
                    maxOverallValueForCurrentPass = theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]
                    maxEvidenceCountForCurrentPass = evidenceCountsMatrix[(i, j)]
                    indexPairWithStrongestTieForCurrentPass = [i, j]
                    
        if maxEvidenceCountForCurrentPass > 0:
            nounAlignments.append(indexPairWithStrongestTieForCurrentPass)
            sourceWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[0])
            targetWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[1])
            for item in relativeAlignmentsMatrix[(indexPairWithStrongestTieForCurrentPass[0], indexPairWithStrongestTieForCurrentPass[1])]:
                if item[0] != 0 and item[1] != 0 and item[0] not in sourceWordIndicesAlreadyAligned and item[1] not in targetWordIndicesAlreadyAligned:
                    nounAlignments.append(item)
                    sourceWordIndicesAlreadyAligned.append(item[0])
                    targetWordIndicesAlreadyAligned.append(item[1])
        else:
            break



    return nounAlignments
##############################################################################################################################




##############################################################################################################################
def alignMainVerbs(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]

    global ppdbSim
    global theta1

    mainVerbAlignments = []

    sourceWordIndices = [i+1 for i in range(len(source))]
    targetWordIndices = [i+1 for i in range(len(target))]

    sourceWordIndicesAlreadyAligned = []
    targetWordIndicesAlreadyAligned = []

    sourceWords = [item[0] for item in source]
    targetWords = [item[0] for item in target]

    sourceLemmas = [item[0] for item in source]
    targetLemmas = [item[0] for item in target]

    sourcePosTags = [item[0] for item in source]
    targetPosTags = [item[0] for item in target]

    


    numberOfMainVerbsInSource = 0

    evidenceCountsMatrix = {}
    relativeAlignmentsMatrix = {}
    wordSimilarities = {}



    # construct the two matrices in the following loop
    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned or sourcePosTags[i-1][0].lower() != 'v' or sourceLemmas[i-1] in stop_words:
            continue

        numberOfMainVerbsInSource += 1
        
        for j in targetWordIndices:
            if j in targetWordIndicesAlreadyAligned or targetPosTags[j-1][0].lower() != 'v' or targetLemmas[j-1] in stop_words:
                continue

            if max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))<ppdbSim:
                continue

            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))
                

            sourceWordParents = findParents(sourceDParse, i, sourceWords[i-1])
            sourceWordChildren = findChildren(sourceDParse, i, sourceWords[i-1])
            targetWordParents = findParents(targetDParse, j, targetWords[j-1])
            targetWordChildren = findChildren(targetDParse, j, targetWords[j-1])




            # search for common or equivalent children
            group1OfSimilarRelationsForNounChild = ['agent', 'nsubj' 'xsubj']
            group2OfSimilarRelationsForNounChild = ['ccomp', 'dobj' 'nsubjpass', 'rel', 'partmod']
            group3OfSimilarRelationsForNounChild = ['tmod', 'prep_in', 'prep_at', 'prep_on']
            group4OfSimilarRelationsForNounChild = ['iobj', 'prep_to']
            groupOfSimilarRelationsForVerbChild = ['purpcl', 'xcomp']


            for ktem in sourceWordChildren:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in mainVerbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in group1OfSimilarRelationsForNounChild and ltem[2] in group1OfSimilarRelationsForNounChild) or
                            (ktem[2] in group2OfSimilarRelationsForNounChild and ltem[2] in group2OfSimilarRelationsForNounChild) or
                            (ktem[2] in group3OfSimilarRelationsForNounChild and ltem[2] in group3OfSimilarRelationsForNounChild) or
                            (ktem[2] in group4OfSimilarRelationsForNounChild and ltem[2] in group4OfSimilarRelationsForNounChild) or                                
                            (ktem[2] in groupOfSimilarRelationsForVerbChild and ltem[2] in groupOfSimilarRelationsForVerbChild)):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])


            # search for common or equivalent parents
            groupOfSimilarRelationsForNounParent = ['infmod', 'partmod', 'rcmod']
            groupOfSimilarRelationsForVerbParent = ['purpcl', 'xcomp']

            for ktem in sourceWordParents:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in  mainVerbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsForNounParent and ltem[2] in groupOfSimilarRelationsForNounParent) or
                            (ktem[2] in groupOfSimilarRelationsForVerbParent and ltem[2] in groupOfSimilarRelationsForVerbParent)):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])




            # search for equivalent parent-child pairs
            groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild = [['cop', 'csubj'], ['acomp']]
            group1OfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['csubj'], ['csubjpass']]            
            group2OfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['conj_and'], ['conj_and']]
            group3OfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['conj_or'], ['conj_or']]
            group4OfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['conj_nor'], ['conj_nor']]


            for ktem in sourceWordParents:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in mainVerbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1]) or
                            (ktem[2] in group4OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in group4OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for equivalent child-parent pairs
            for ktem in sourceWordChildren:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in mainVerbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0]) or
                            (ktem[2] in group4OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in group4OfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])


    # now use the collected stats to align
    for n in range(numberOfMainVerbsInSource):

        maxEvidenceCountForCurrentPass = 0
        maxOverallValueForCurrentPass = 0
        indexPairWithStrongestTieForCurrentPass = [-1, -1]


        for i in sourceWordIndices:
            if i in sourceWordIndicesAlreadyAligned or sourcePosTags[i-1][0].lower() != 'v' or sourceLemmas[i-1] in stop_words:
                continue

            for j in targetWordIndices:
                if j in targetWordIndicesAlreadyAligned or targetPosTags[j-1][0].lower() != 'v' or targetLemmas[j-1] in stop_words:
                    continue

                if (i, j) in evidenceCountsMatrix and theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]>maxOverallValueForCurrentPass:
                    maxOverallValueForCurrentPass = theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]
                    maxEvidenceCountForCurrentPass = evidenceCountsMatrix[(i, j)]
                    indexPairWithStrongestTieForCurrentPass = [i, j]
                    
        if maxEvidenceCountForCurrentPass > 0:
            mainVerbAlignments.append(indexPairWithStrongestTieForCurrentPass)
            sourceWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[0])
            targetWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[1])
            for item in relativeAlignmentsMatrix[(indexPairWithStrongestTieForCurrentPass[0], indexPairWithStrongestTieForCurrentPass[1])]:
                if item[0] != 0 and item[1] != 0 and item[0] not in sourceWordIndicesAlreadyAligned and item[1] not in targetWordIndicesAlreadyAligned:
                    mainVerbAlignments.append(item)
                    sourceWordIndicesAlreadyAligned.append(item[0])
                    targetWordIndicesAlreadyAligned.append(item[1])
        else:
            break



    return mainVerbAlignments
##############################################################################################################################


##############################################################################################################################
def alignAdjectives(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]

    global ppdbSim
    global theta1


    adjectiveAlignments = []

    sourceWordIndices = [i+1 for i in range(len(source))]
    targetWordIndices = [i+1 for i in range(len(target))]

    sourceWordIndicesAlreadyAligned = []
    targetWordIndicesAlreadyAligned = []

    sourceWords = [item[2] for item in source]
    targetWords = [item[2] for item in target]

    sourceLemmas = [item[3] for item in source]
    targetLemmas = [item[3] for item in target]

    sourcePosTags = [item[4] for item in source]
    targetPosTags = [item[4] for item in target]


    numberOfAdjectivesInSource = 0

    evidenceCountsMatrix = {}
    relativeAlignmentsMatrix = {}
    wordSimilarities = {}


    # construct the two matrices in the following loop
    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned or sourcePosTags[i-1][0].lower() != 'j':
            continue

        numberOfAdjectivesInSource += 1    

        for j in targetWordIndices:
            if j in targetWordIndicesAlreadyAligned or targetPosTags[j-1][0].lower() != 'j':
                continue


            if max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))<ppdbSim:
                continue


            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))


            sourceWordParents = findParents(sourceDParse, i, sourceWords[i-1])
            sourceWordChildren = findChildren(sourceDParse, i, sourceWords[i-1])
            targetWordParents = findParents(targetDParse, j, targetWords[j-1])
            targetWordChildren = findChildren(targetDParse, j, targetWords[j-1])


            # search for common or equivalent parents
            groupOfSimilarRelationsForNounParent = ['amod', 'rcmod']

            for ktem in sourceWordParents:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in adjectiveAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and ((ktem[2]==ltem[2]) or (ktem[2] in groupOfSimilarRelationsForNounParent and ltem[2] in groupOfSimilarRelationsForNounParent)):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])

                        


            # search for common children
            for ktem in sourceWordChildren:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in adjectiveAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (ktem[2]==ltem[2]):
                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for equivalent parent-child pair
            groupOfSimilarRelationsInOppositeDirectionForNounParentAndChild = [['amod', 'rcmod'], ['nsubj']]
            groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild = [['acomp'], ['cop', 'csubj']]
            group1OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild = [['conj_and'], ['conj_and']]
            group2OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild = [['conj_or'], ['conj_or']]
            group3OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild = [['conj_nor'], ['conj_nor']]


            for ktem in sourceWordParents:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in adjectiveAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForNounParentAndChild[0] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForNounParentAndChild[1]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])


            # search for equivalent child-parent pair
            for ktem in sourceWordChildren:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in adjectiveAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForNounParentAndChild[1] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForNounParentAndChild[0]) or
                            (ktem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[1] and ltem[2] in groupOfSimilarRelationsInOppositeDirectionForVerbParentAndChild[0]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[1] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForAdjectiveParentAndChild[0])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])


    # now use the collected stats to align
    for n in range(numberOfAdjectivesInSource):

        maxEvidenceCountForCurrentPass = 0
        maxOverallValueForCurrentPass = 0
        indexPairWithStrongestTieForCurrentPass = [-1, -1]


        for i in sourceWordIndices:
            if i in sourceWordIndicesAlreadyAligned or sourcePosTags[i-1][0].lower() != 'j' or sourceLemmas[i-1] in stop_words:
                continue

            for j in targetWordIndices:
                if j in targetWordIndicesAlreadyAligned or targetPosTags[j-1][0].lower() != 'j' or targetLemmas[j-1] in stop_words:
                    continue

                if (i, j) in evidenceCountsMatrix and theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]>maxOverallValueForCurrentPass:
                    maxOverallValueForCurrentPass = theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]
                    maxEvidenceCountForCurrentPass = evidenceCountsMatrix[(i, j)]
                    indexPairWithStrongestTieForCurrentPass = [i, j]
                    
        if maxEvidenceCountForCurrentPass > 0:
            adjectiveAlignments.append(indexPairWithStrongestTieForCurrentPass)
            sourceWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[0])
            targetWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[1])
            for item in relativeAlignmentsMatrix[(indexPairWithStrongestTieForCurrentPass[0], indexPairWithStrongestTieForCurrentPass[1])]:
                if item[0] != 0 and item[1] != 0 and item[0] not in sourceWordIndicesAlreadyAligned and item[1] not in targetWordIndicesAlreadyAligned:
                    adjectiveAlignments.append(item)
                    sourceWordIndicesAlreadyAligned.append(item[0])
                    targetWordIndicesAlreadyAligned.append(item[1])
        else:
            break



    return adjectiveAlignments
##############################################################################################################################



##############################################################################################################################
def alignAdverbs(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]

    global ppdbSim
    global theta1


    adverbAlignments = []

    sourceWordIndices = [i+1 for i in range(len(source))]
    targetWordIndices = [i+1 for i in range(len(target))]

    sourceWordIndicesAlreadyAligned =[] 
    targetWordIndicesAlreadyAligned =[] 

    sourceWords = [item[2] for item in source]
    targetWords = [item[2] for item in target]

    sourceLemmas = [item[3] for item in source]
    targetLemmas = [item[3] for item in target]

    sourcePosTags = [item[4] for item in source]
    targetPosTags = [item[4] for item in target]


    numberOfAdverbsInSource = 0

    evidenceCountsMatrix = {}
    relativeAlignmentsMatrix = {}
    wordSimilarities = {}


    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned or (sourcePosTags[i-1][0].lower() != 'r'):
            continue

        numberOfAdverbsInSource += 1

        for j in targetWordIndices:
            if j in targetWordIndicesAlreadyAligned or (targetPosTags[j-1][0].lower() != 'r'):
                continue


            if max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))<ppdbSim:
                continue

            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))


            sourceWordParents = findParents(sourceDParse, i, sourceWords[i-1])
            sourceWordChildren = findChildren(sourceDParse, i, sourceWords[i-1])
            targetWordParents = findParents(targetDParse, j, targetWords[j-1])
            targetWordChildren = findChildren(targetDParse, j, targetWords[j-1])


            # search for common parents            
            for ktem in sourceWordParents:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in adverbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (ktem[2]==ltem[2]):
                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])


            # search for common children            
            for ktem in sourceWordChildren:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in adverbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (ktem[2]==ltem[2]):
                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])

                        
            # search for equivalent parent-child relationships
            group1OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild = [['conj_and'], ['conj_and']]
            group2OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild = [['conj_or'], ['conj_or']]
            group3OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild = [['conj_nor'], ['conj_nor']]

            for ktem in sourceWordParents:
                for ltem in targetWordChildren:
                    if ((ktem[0], ltem[0]) in adverbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



            # search for equivalent child-parent relationships
            for ktem in sourceWordChildren:
                for ltem in targetWordParents:
                    if ((ktem[0], ltem[0]) in adverbAlignments or max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))>=ppdbSim) and (
                            (ktem[2]==ltem[2]) or
                            (ktem[2] in group1OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1] and ltem[2] in group1OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0]) or
                            (ktem[2] in group2OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1] and ltem[2] in group2OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0]) or
                            (ktem[2] in group3OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[1] and ltem[2] in group3OfSimilarRelationsInOppositeDirectionForAdverbParentAndChild[0])):

                        if (i, j) in evidenceCountsMatrix:
                            evidenceCountsMatrix[(i, j)] += max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))
                        else:
                            evidenceCountsMatrix[(i, j)] = max(wordRelatedness(ktem[1], sourcePosTags[ktem[0]-1], ltem[1], targetPosTags[ltem[0]-1]), wordRelatedness(sourceLemmas[ktem[0]-1], sourcePosTags[ktem[0]-1], targetLemmas[ltem[0]-1], targetPosTags[ltem[0]-1]))

                        if (i, j) in relativeAlignmentsMatrix:
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])
                        else:
                            relativeAlignmentsMatrix[(i, j)] = []
                            relativeAlignmentsMatrix[(i, j)].append([ktem[0], ltem[0]])



    # now use the collected stats to align
    for n in range(numberOfAdverbsInSource):

        maxEvidenceCountForCurrentPass = 0
        maxOverallValueForCurrentPass = 0
        indexPairWithStrongestTieForCurrentPass = [-1, -1]


        for i in sourceWordIndices:
            if i in sourceWordIndicesAlreadyAligned or sourcePosTags[i-1][0].lower() != 'r' or sourceLemmas[i-1] in stop_words:
                continue

            for j in targetWordIndices:
                if j in targetWordIndicesAlreadyAligned or targetPosTags[j-1][0].lower() != 'r' or targetLemmas[j-1] in stop_words:
                    continue

                if (i, j) in evidenceCountsMatrix and theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]>maxOverallValueForCurrentPass:
                    maxOverallValueForCurrentPass = theta1*wordSimilarities[(i, j)]+(1-theta1)*evidenceCountsMatrix[(i, j)]
                    maxEvidenceCountForCurrentPass = evidenceCountsMatrix[(i, j)]
                    indexPairWithStrongestTieForCurrentPass = [i, j]
                    
        if maxEvidenceCountForCurrentPass > 0:
            adverbAlignments.append(indexPairWithStrongestTieForCurrentPass)
            sourceWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[0])
            targetWordIndicesAlreadyAligned.append(indexPairWithStrongestTieForCurrentPass[1])
            for item in relativeAlignmentsMatrix[(indexPairWithStrongestTieForCurrentPass[0], indexPairWithStrongestTieForCurrentPass[1])]:
                if item[0] != 0 and item[1] != 0 and item[0] not in sourceWordIndicesAlreadyAligned and item[1] not in targetWordIndicesAlreadyAligned:
                    adverbAlignments.append(item)
                    sourceWordIndicesAlreadyAligned.append(item[0])
                    targetWordIndicesAlreadyAligned.append(item[1])
        else:
            break



    return adverbAlignments
##############################################################################################################################


##############################################################################################################################
def alignNamedEntities(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]

    global punctuations

    alignments = []

   
    sourceNamedEntities = []
    
   
    targetNamedEntities = []
    

    # learn from the other sentence that a certain word/phrase is a named entity (learn for source from target)
    for item in source:
        alreadyIncluded = False
        for jtem in sourceNamedEntities:
            if item[1] in jtem[1]:
                alreadyIncluded = True
                break
        if alreadyIncluded or (len(item[0]) >0 and not item[0][0].isupper()):
            continue
        for jtem in targetNamedEntities:
            if item[2] in jtem[2]:
                # construct the item
                newItem = [[item[0]], [item[1]], [item[2]], jtem[3]]

                # check if the current item is part of a named entity part of which has already been added (by checking contiguousness)
                partOfABiggerName = False
                for k in range(len(sourceNamedEntities)):
                    if sourceNamedEntities[k][1][len(sourceNamedEntities[k][1])-1] == newItem[1][0] - 1:
                        sourceNamedEntities[k][0].append(newItem[0][0])
                        sourceNamedEntities[k][1].append(newItem[1][0])
                        sourceNamedEntities[k][2].append(newItem[2][0])
                        partOfABiggerName = True
                if not partOfABiggerName:
                    sourceNamedEntities.append(newItem)
            elif isAcronym(item[2], jtem[2]) and [[item[0]], [item[1]], [item[2]], jtem[3]] not in sourceNamedEntities:
                sourceNamedEntities.append([[item[0]], [item[1]], [item[2]], jtem[3]])


    
    # learn from the other sentence that a certain word/phrase is a named entity (learn for target from source)
    for item in target:
        alreadyIncluded = False
        for jtem in targetNamedEntities:
            if item[1] in jtem[1]:
                alreadyIncluded = True
                break
        if alreadyIncluded or (len(item[0]) >0 and not item[0][0].isupper()):
            continue
        for jtem in sourceNamedEntities:
            if item[2] in jtem[2]:
                # construct the item
                newItem = [[item[0]], [item[1]], [item[2]], jtem[3]]

                # check if the current item is part of a named entity part of which has already been added (by checking contiguousness)
                partOfABiggerName = False
                for k in range(len(targetNamedEntities)):
                    if targetNamedEntities[k][1][len(targetNamedEntities[k][1])-1] == newItem[1][0] - 1:
                        targetNamedEntities[k][0].append(newItem[0][0])
                        targetNamedEntities[k][1].append(newItem[1][0])
                        targetNamedEntities[k][2].append(newItem[2][0])
                        partOfABiggerName = True
                if not partOfABiggerName:
                    targetNamedEntities.append(newItem)
            elif isAcronym(item[2], jtem[2]) and [[item[0]], [item[1]], [item[2]], jtem[3]] not in targetNamedEntities:
                targetNamedEntities.append([[item[0]], [item[1]], [item[2]], jtem[3]])
    

    sourceWords = []
    targetWords = []

    for item in sourceNamedEntities:
        for jtem in item[1]:
            if item[3] in ['PERSON', 'ORGANIZATION', 'LOCATION']:
                sourceWords.append(source[jtem-1][2])
    for item in targetNamedEntities:
        for jtem in item[1]:
            if item[3] in ['PERSON', 'ORGANIZATION', 'LOCATION']:
                targetWords.append(target[jtem-1][2])



    if len(sourceNamedEntities) == 0 or len(targetNamedEntities) == 0:
        return []




    sourceNamedEntitiesAlreadyAligned = []
    targetNamedEntitiesAlreadyAligned = []
    

    # align all full matches
    for item in sourceNamedEntities:
        if item[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION']:
            continue

        
        # do not align if the current source entity is present more than once
        count = 0
        for ktem in sourceNamedEntities:
            if ktem[2] == item[2]:
                count += 1
        if count > 1:
            continue


        for jtem in targetNamedEntities:
            if jtem[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION']:
                continue

            # do not align if the current target entity is present more than once
            count = 0
            for ktem in targetNamedEntities:
                if ktem[2] == jtem[2]:
                    count += 1
            if count > 1:
                continue


            # get rid of dots and hyphens
            canonicalItemWord = [i.replace('.', '') for i in item[2]]
            canonicalItemWord = [i.replace('-', '') for i in item[2]]
            canonicalJtemWord = [j.replace('.', '') for j in jtem[2]]
            canonicalJtemWord = [j.replace('-', '') for j in jtem[2]]

            if canonicalItemWord == canonicalJtemWord:
                for k in xrange(len(item[1])):
                    if ([item[1][k], jtem[1][k]]) not in alignments:
                        alignments.append([item[1][k], jtem[1][k]])
                sourceNamedEntitiesAlreadyAligned.append(item)
                targetNamedEntitiesAlreadyAligned.append(jtem)

    # align acronyms with their elaborations
    for item in sourceNamedEntities:
        if item[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION']:
            continue
        for jtem in targetNamedEntities:
            if jtem[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION']:
                continue

            if len(item[2])==1 and isAcronym(item[2][0], jtem[2]):
                for i in range(len(jtem[1])):
                    if [item[1][0], jtem[1][i]] not in alignments:
                        alignments.append([item[1][0], jtem[1][i]])
                        sourceNamedEntitiesAlreadyAligned.append(item[1][0])
                        targetNamedEntitiesAlreadyAligned.append(jtem[1][i])

            elif len(jtem[2])==1 and isAcronym(jtem[2][0], item[2]):
                for i in xrange(len(item[1])):
                    if [item[1][i], jtem[1][0]] not in alignments:
                        alignments.append([item[1][i], jtem[1][0]])
                        sourceNamedEntitiesAlreadyAligned.append(item[1][i])
                        targetNamedEntitiesAlreadyAligned.append(jtem[1][0])

                               
    # align subset matches
    for item in sourceNamedEntities:
        if item[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION'] or item in sourceNamedEntitiesAlreadyAligned:
            continue

        # do not align if the current source entity is present more than once
        count = 0
        for ktem in sourceNamedEntities:
            if ktem[2] == item[2]:
                count += 1
        if count > 1:
            continue


        for jtem in targetNamedEntities:
            if jtem[3] not in ['PERSON', 'ORGANIZATION', 'LOCATION'] or jtem in targetNamedEntitiesAlreadyAligned:
                continue

            if item[3] != jtem[3]:
                continue

            # do not align if the current target entity is present more than once
            count = 0
            for ktem in targetNamedEntities:
                if ktem[2] == jtem[2]:
                    count += 1
            if count > 1:
                continue

            
            # find if the first is a part of the second
            if isSublist(item[2], jtem[2]):
                unalignedWordIndicesInTheLongerName = []
                for ktem in jtem[1]:
                    unalignedWordIndicesInTheLongerName.append(ktem)
                for k in range(len(item[2])):
                    for l in range(len(jtem[2])):
                        if item[2][k] == jtem[2][l] and [item[1][k], jtem[1][l]] not in alignments:
                            alignments.append([item[1][k], jtem[1][l]])
                            if jtem[1][l] in unalignedWordIndicesInTheLongerName:
                                unalignedWordIndicesInTheLongerName.remove(jtem[1][l])
                for k in range(len(item[1])): # the shorter name
                    for l in range(len(jtem[1])): # the longer name
                        # find if the current term in the longer name has already been aligned (before calling alignNamedEntities()), do not align it in that case
                        alreadyInserted = False
                        for mtem in existingAlignments:
                            if mtem[1] == jtem[1][l]:
                                alreadyInserted = True
                                break
                        if jtem[1][l] not in unalignedWordIndicesInTheLongerName or alreadyInserted:
                            continue
                        if [item[1][k], jtem[1][l]] not in alignments  and target[jtem[1][l]-1][2] not in sourceWords  and item[2][k] not in punctuations and jtem[2][l] not in punctuations:
                            alignments.append([item[1][k], jtem[1][l]])
                
            # else find if the second is a part of the first
            elif isSublist(jtem[2], item[2]):
                unalignedWordIndicesInTheLongerName = []
                for ktem in item[1]:
                    unalignedWordIndicesInTheLongerName.append(ktem)
                for k in xrange(len(jtem[2])):
                    for l in range(len(item[2])):
                        if jtem[2][k] == item[2][l] and [item[1][l], jtem[1][k]] not in alignments:
                            alignments.append([item[1][l], jtem[1][k]])
                            if item[1][l] in unalignedWordIndicesInTheLongerName:
                                unalignedWordIndicesInTheLongerName.remove(item[1][l])
                for k in range(len(jtem[1])): # the shorter name
                    for l in range(len(item[1])): # the longer name
                        # find if the current term in the longer name has already been aligned (before calling alignNamedEntities()), do not align it in that case
                        alreadyInserted = False
                        for mtem in existingAlignments:
                            if mtem[0] == item[1][k]:
                                alreadyInserted = True
                                break
                        if item[1][l] not in unalignedWordIndicesInTheLongerName or alreadyInserted:
                            continue
                        if [item[1][l], jtem[1][k]] not in alignments  and source[item[1][k]-1][2] not in targetWords  and item[2][l] not in punctuations and jtem[2][k] not in punctuations:
                            alignments.append([item[1][l], jtem[1][k]])
                            #unalignedWordIndicesInTheLongerName.remove(jtem[1][l])

                

    return alignments
##############################################################################################################################




##############################################################################################################################
def alignWords(source, target):
# source and target:: each is a list of elements of the form:
# [[character begin offset, character end offset], word index, word, lemma, pos tag]

# function returns the word alignments from source to target - each alignment returned is of the following form:
# [
#  [[source word character begin offset, source word character end offset], source word index, source word, source word lemma],
#  [[target word character begin offset, target word character end offset], target word index, target word, target word lemma]
# ]

    global punctuations

    sourceWordIndices = [i+1 for i in range(len(source))]
    targetWordIndices = [i+1 for i in range(len(target))]


    alignments = []

    sequenceAlignments = []
    namedEntityAlignments = []
    dependencyNeighborhoodAlignments = []
    textualNeighborhoodAlignments = []
    noContextualOverlapAlignments = []

    sourceWordIndicesAlreadyAligned= []
    targetWordIndicesAlreadyAligned= []
     
    #print(source)
    #print("target is",target)
     
    #so = [item[0] for item in source]
    #ta = [item[0] for item in target]
      
    sourceWords = [item[0] for item in source]
    targetWords = [item[0] for item in target]

    sourceLemmas = [item[0] for item in source]
    targetLemmas = [item[0] for item in target]

    sourcePosTags = [item[0] for item in source]
    targetPosTags = [item[0] for item in target]






    # align the sentence ending punctuation first
    if (sourceWords[len(source)-1] in ['.', '!'] and targetWords[len(target)-1] in ['.', '!']) or sourceWords[len(source)-1]==targetWords[len(target)-1]:
        alignments.append([len(source), len(target)])
        noContextualOverlapAlignments.append([len(source), len(target)])
        sourceWordIndicesAlreadyAligned.append(len(source))
        targetWordIndicesAlreadyAligned.append(len(target))
    elif (sourceWords[len(source)-2] in ['.', '!'] and targetWords[len(target)-1] in ['.', '!']):
        alignments.append([len(source)-1, len(target)])
        noContextualOverlapAlignments.append([len(source)-1, len(target)])
        sourceWordIndicesAlreadyAligned.append(len(source)-1)
        targetWordIndicesAlreadyAligned.append(len(target))
    elif sourceWords[len(source)-1] in ['.', '!'] and targetWords[len(target)-2]  in ['.', '!']:
        alignments.append([len(source), len(target)-1])
        noContextualOverlapAlignments.append([len(source), len(target)-1])
        sourceWordIndicesAlreadyAligned.append(len(source))
        targetWordIndicesAlreadyAligned.append(len(target)-1)
    elif sourceWords[len(source)-2] in ['.', '!'] and targetWords[len(target)-2]  in ['.', '!']:
        alignments.append([len(source)-1, len(target)-1])
        noContextualOverlapAlignments.append([len(source)-1, len(target)-1])
        sourceWordIndicesAlreadyAligned.append(len(source)-1)
        targetWordIndicesAlreadyAligned.append(len(target)-1)
    
    




    

    # align all (>=2)-gram matches with at least one content word
    commonContiguousSublists  = findAllCommonContiguousSublists(sourceWords, targetWords, True)
    for item in commonContiguousSublists:
        allstop_words = True
        for jtem in item:
            if jtem not in stop_words and jtem not in punctuations:
                allstop_words = False
                break
        if len(item[0]) >= 2 and not allstop_words:
            for j in range(len(item[0])):
                if item[0][j]+1 not in sourceWordIndicesAlreadyAligned and item[1][j]+1 not in targetWordIndicesAlreadyAligned and [item[0][j]+1, item[1][j]+1] not in alignments:
                    alignments.append([item[0][j]+1, item[1][j]+1])
                    sequenceAlignments.append([item[0][j]+1, item[1][j]+1])
                    sourceWordIndicesAlreadyAligned.append(item[0][j]+1)
                    targetWordIndicesAlreadyAligned.append(item[1][j]+1)
  








    
    # align hyphenated word groups
    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned:
            continue
        if '-' in sourceWords[i-1] and sourceWords[i-1] != '-':
            tokens = sourceWords[i-1].split('-')
            commonContiguousSublists = findAllCommonContiguousSublists(tokens, targetWords)
            for item in commonContiguousSublists:
                if len(item[0]) > 1:
                    for jtem in item[1]:
                        if [i, jtem+1] not in alignments:
                            alignments.append([i, jtem+1])
                            sequenceAlignments.append([i, jtem+1])
                            sourceWordIndicesAlreadyAligned.append(i)
                            targetWordIndicesAlreadyAligned.append(jtem+1)
            
    for i in targetWordIndices:
        if i in targetWordIndicesAlreadyAligned:
            continue
        if '-' in target[i-1][0] and target[i-1][0] != '-':
            tokens = target[i-1][2].split('-')
            commonContiguousSublists = findAllCommonContiguousSublists(sourceWords, tokens)
            for item in commonContiguousSublists:
                if len(item[0]) > 1:
                    for jtem in item[0]:
                        if [jtem+1, i] not in alignments:
                            alignments.append([jtem+1, i])
                            sequenceAlignments.append([jtem+1, i])
                            sourceWordIndicesAlreadyAligned.append(jtem+1)
                            targetWordIndicesAlreadyAligned.append(i)









    
    
    # align named entities
    neAlignments = alignNamedEntities(source, target)
    for item in neAlignments:
        if item not in alignments:
            alignments.append(item)
            namedEntityAlignments.append(item)
            if item[0] not in sourceWordIndicesAlreadyAligned:
                sourceWordIndicesAlreadyAligned.append(item[0])
            if item[1] not in targetWordIndicesAlreadyAligned:
                targetWordIndicesAlreadyAligned.append(item[1])
    


    

           
    

    
    # align words based on word and dependency match
   
    mainVerbAlignments = alignMainVerbs(source, target)
    for item in mainVerbAlignments:
        if item not in alignments:
            alignments.append(item)
            dependencyNeighborhoodAlignments.append(item)
            if item[0] not in sourceWordIndicesAlreadyAligned:
                sourceWordIndicesAlreadyAligned.append(item[0])
            if item[1] not in targetWordIndicesAlreadyAligned:
                targetWordIndicesAlreadyAligned.append(item[1])

    
    nounAlignments = alignNouns(source, target)
    for item in nounAlignments:
        if item not in alignments:
            alignments.append(item)
            dependencyNeighborhoodAlignments.append(item)
            if item[0] not in sourceWordIndicesAlreadyAligned:
                sourceWordIndicesAlreadyAligned.append(item[0])
            if item[1] not in targetWordIndicesAlreadyAligned:
                targetWordIndicesAlreadyAligned.append(item[1])

    adjectiveAlignments = alignAdjectives(source, target)
    for item in adjectiveAlignments:
        if item not in alignments:
            alignments.append(item)
            dependencyNeighborhoodAlignments.append(item)
            if item[0] not in sourceWordIndicesAlreadyAligned:
                sourceWordIndicesAlreadyAligned.append(item[0])
            if item[1] not in targetWordIndicesAlreadyAligned:
                targetWordIndicesAlreadyAligned.append(item[1])

    adverbAlignments = alignAdverbs(source, target)
    for item in adverbAlignments:
        if item not in alignments:
            alignments.append(item)
            dependencyNeighborhoodAlignments.append(item)
            if item[0] not in sourceWordIndicesAlreadyAligned:
                sourceWordIndicesAlreadyAligned.append(item[0])
            if item[1] not in targetWordIndicesAlreadyAligned:
                targetWordIndicesAlreadyAligned.append(item[1])
    
        
            










        
    # collect evidence from textual neighborhood for aligning content words
    wordSimilarities = {}
    textualNeighborhoodSimilarities = {}
    sourceWordIndicesBeingConsidered = []
    targetWordIndicesBeingConsidered = []


    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned or sourceLemmas[i-1] in stop_words + punctuations + ['\'s', '\'d', '\'ll']:
            continue
        
        for j in targetWordIndices:
            if j in targetWordIndicesAlreadyAligned or targetLemmas[j-1] in stop_words + punctuations + ['\'s', '\'d', '\'ll']:
                continue

            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))
            sourceWordIndicesBeingConsidered.append(i)
            targetWordIndicesBeingConsidered.append(j)


            # textual neighborhood similarities
            sourceNeighborhood = findTextualNeighborhood(source, i, 3, 3)
            targetNeighborhood = findTextualNeighborhood(target, j, 3, 3)
            evidence = 0
            for k in xrange(len(sourceNeighborhood[0])):
                for l in xrange(len(targetNeighborhood[0])):
                      if (sourceNeighborhood[1][k] not in stop_words + punctuations) and ((sourceNeighborhood[0][k], targetNeighborhood[0][l]) in alignments or (wordRelatedness(sourceNeighborhood[1][k], 'none', targetNeighborhood[1][l], 'none')>=ppdbSim)):
                        evidence += wordRelatedness(sourceNeighborhood[1][k], 'none', targetNeighborhood[1][l], 'none')
            textualNeighborhoodSimilarities[(i, j)] = evidence

    
    numOfUnalignedWordsInSource = len(sourceWordIndicesBeingConsidered)   

    # now align: find the best alignment in each iteration of the following loop and include in alignments if good enough
    for item in range(numOfUnalignedWordsInSource):
        highestWeightedSim = 0
        bestWordSim = 0
        bestSourceIndex = -1
        bestTargetIndex = -1
        
        for i in sourceWordIndicesBeingConsidered:
            if i in sourceWordIndicesAlreadyAligned:
                continue
            
            for j in targetWordIndicesBeingConsidered:
                if j in targetWordIndicesAlreadyAligned:
                    continue

                if (i, j) not in wordSimilarities:
                    continue
                
                theta2 = 1 - theta1
                if theta1*wordSimilarities[(i, j)] + theta2*textualNeighborhoodSimilarities[(i, j)] > highestWeightedSim:
                    highestWeightedSim = theta1*wordSimilarities[(i, j)] + theta2*textualNeighborhoodSimilarities[(i, j)]
                    bestSourceIndex = i
                    bestTargetIndex = j
                    bestWordSim = wordSimilarities[(i, j)]
                    bestTextNeighborhoodSim = textualNeighborhoodSimilarities[(i, j)]

        if bestWordSim>=ppdbSim and [bestSourceIndex, bestTargetIndex] not in alignments:
            if sourceLemmas[bestSourceIndex-1] not in stop_words:
                alignments.append([bestSourceIndex, bestTargetIndex])
                if bestTextNeighborhoodSim > 0:
                    textualNeighborhoodAlignments.append([bestSourceIndex, bestTargetIndex])
                else:
                    noContextualOverlapAlignments.append([bestSourceIndex, bestTargetIndex])
                sourceWordIndicesAlreadyAligned.append(bestSourceIndex)
                targetWordIndicesAlreadyAligned.append(bestTargetIndex)

        if bestSourceIndex in sourceWordIndicesBeingConsidered:    
            sourceWordIndicesBeingConsidered.remove(bestSourceIndex)
        if bestTargetIndex in targetWordIndicesBeingConsidered:
            targetWordIndicesBeingConsidered.remove(bestTargetIndex)











    # look if any remaining word is a part of a hyphenated word
    for i in sourceWordIndices:
        if i in sourceWordIndicesAlreadyAligned:
            continue
        if '-' in sourceWords[i-1] and sourceWords[i-1] != '-':
            tokens = sourceWords[i-1].split('-')
            commonContiguousSublists = findAllCommonContiguousSublists(tokens, targetWords)
            for item in commonContiguousSublists:
                if len(item[0]) == 1 and target[item[1][0]][3] not in stop_words:
                    for jtem in item[1]:
                        if [i, jtem+1] not in alignments and jtem+1 not in targetWordIndicesAlreadyAligned:
                            alignments.append([i, jtem+1])
                            noContextualOverlapAlignments.append([i, jtem+1])
                            sourceWordIndicesAlreadyAligned.append(i)
                            targetWordIndicesAlreadyAligned.append(jtem+1)
            
    for i in targetWordIndices:
        if i in targetWordIndicesAlreadyAligned:
            continue
        if '-' in target[i-1][2] and target[i-1][2] != '-':
            tokens = target[i-1][2].split('-')
            commonContiguousSublists = findAllCommonContiguousSublists(sourceWords, tokens)
            for item in commonContiguousSublists:
                if len(item[0]) == 1 and source[item[0][0]][3] not in stop_words:
                    for jtem in item[0]:
                        if [jtem+1, i] not in alignments and i not in targetWordIndicesAlreadyAligned:
                            alignments.append([jtem+1, i])
                            noContextualOverlapAlignments.append([jtem+1, i])
                            sourceWordIndicesAlreadyAligned.append(jtem+1)
                            targetWordIndicesAlreadyAligned.append(i)
    





   

                

            
    '''
    # collect evidence from dependency neighborhood for aligning stop_words
    wordSimilarities = {}
    dependencyNeighborhoodSimilarities = {}
    sourceWordIndicesBeingConsidered = []
    targetWordIndicesBeingConsidered = []

    for i in sourceWordIndices:
        if sourceLemmas[i-1] not in stop_words or i in sourceWordIndicesAlreadyAligned:
            continue
        
        for j in targetWordIndices:
            if targetLemmas[j-1] not in stop_words or j in targetWordIndicesAlreadyAligned:
                continue

            if (sourceLemmas[i-1]<>targetLemmas[j-1]) and (wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1])<ppdbSim):
                continue

            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))

            sourceWordIndicesBeingConsidered.append(i)
            targetWordIndicesBeingConsidered.append(j)

            sourceWordParents = findParents(sourceDParse, i, sourceWords[i-1])
            sourceWordChildren = findChildren(sourceDParse, i, sourceWords[i-1])
            targetWordParents = findParents(targetDParse, j, targetWords[j-1])
            targetWordChildren = findChildren(targetDParse, j, targetWords[j-1])

            evidence = 0

            for item in sourceWordParents:
                for jtem in targetWordParents:
                    if [item[0], jtem[0]] in alignments:
                        evidence += 1
            for item in sourceWordChildren:
                for jtem in targetWordChildren:
                    if [item[0], jtem[0]] in alignments:
                        evidence += 1

            dependencyNeighborhoodSimilarities[(i, j)] = evidence
    
    numOfUnalignedWordsInSource = len(sourceWordIndicesBeingConsidered)   

    # now align: find the best alignment in each iteration of the following loop and include in alignments if good enough
    for item in xrange(numOfUnalignedWordsInSource):
        highestWeightedSim = 0
        bestWordSim = 0
        bestSourceIndex = -1
        bestTargetIndex = -1
        
        for i in sourceWordIndicesBeingConsidered:
            for j in targetWordIndicesBeingConsidered:
                if (i, j) not in wordSimilarities:
                    continue
                theta2 = 1 - theta1
                if theta1*wordSimilarities[(i, j)] + theta2*dependencyNeighborhoodSimilarities[(i, j)] > highestWeightedSim:
                    highestWeightedSim = theta1*wordSimilarities[(i, j)] + theta2*dependencyNeighborhoodSimilarities[(i, j)]
                    bestSourceIndex = i
                    bestTargetIndex = j
                    bestWordSim = wordSimilarities[(i, j)]
                    bestDependencyNeighborhoodSim = dependencyNeighborhoodSimilarities[(i, j)]

        if bestWordSim>=ppdbSim and bestDependencyNeighborhoodSim>0 and [bestSourceIndex, bestTargetIndex] not in alignments:
            alignments.append([bestSourceIndex, bestTargetIndex])
            dependencyNeighborhoodAlignments.append([bestSourceIndex, bestTargetIndex])
            sourceWordIndicesAlreadyAligned.append(bestSourceIndex)
            targetWordIndicesAlreadyAligned.append(bestTargetIndex)

        if bestSourceIndex in sourceWordIndicesBeingConsidered:    
            sourceWordIndicesBeingConsidered.remove(bestSourceIndex)
        if bestTargetIndex in targetWordIndicesBeingConsidered:
            targetWordIndicesBeingConsidered.remove(bestTargetIndex)
    





    # collect evidence from textual neighborhood for aligning stop_words and punctuations
    wordSimilarities = {}
    textualNeighborhoodSimilarities = {}
    sourceWordIndicesBeingConsidered = []
    targetWordIndicesBeingConsidered = []
    
    for i in sourceWordIndices:
        if (sourceLemmas[i-1] not in stop_words + punctuations + ['\'s', '\'d', '\'ll']) or i in sourceWordIndicesAlreadyAligned:
            continue
        
        for j in targetWordIndices:
            if (targetLemmas[j-1] not in stop_words + punctuations + ['\'s', '\'d', '\'ll']) or j in targetWordIndicesAlreadyAligned:
                continue
            
            if wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]) < ppdbSim:
                continue


            wordSimilarities[(i, j)] = max(wordRelatedness(sourceWords[i-1], sourcePosTags[i-1], targetWords[j-1], targetPosTags[j-1]), wordRelatedness(sourceLemmas[i-1], sourcePosTags[i-1], targetLemmas[j-1], targetPosTags[j-1]))

            sourceWordIndicesBeingConsidered.append(i)
            targetWordIndicesBeingConsidered.append(j)

            # textual neighborhood evidence
            evidence = 0

            if [i-1, j-1] in alignments:
                evidence += 1
                
            if [i+1, j+1] in alignments:
                evidence += 1

            try:
                textualNeighborhoodSimilarities[(i, j)] = evidence
            except ZeroDivisionError:
                textualNeighborhoodSimilarities[(i, j)] = 0

    numOfUnalignedWordsInSource = len(sourceWordIndicesBeingConsidered)   

    # now align: find the best alignment in each iteration of the following loop and include in alignments if good enough
    for item in xrange(numOfUnalignedWordsInSource):
        highestWeightedSim = 0
        bestWordSim = 0
        bestSourceIndex = -1
        bestTargetIndex = -1
        
        for i in sourceWordIndicesBeingConsidered:
            if i in sourceWordIndicesAlreadyAligned:
                continue
            
            for j in targetWordIndicesBeingConsidered:
                if j in targetWordIndicesAlreadyAligned:
                    continue
                
                if (i, j) not in wordSimilarities:
                    continue


                theta2 = 1 - theta1
                if theta1*wordSimilarities[(i, j)] + theta2*textualNeighborhoodSimilarities[(i, j)] > highestWeightedSim:
                    highestWeightedSim = theta1*wordSimilarities[(i, j)] + theta2*textualNeighborhoodSimilarities[(i, j)]
                    bestSourceIndex = i
                    bestTargetIndex = j
                    bestWordSim = wordSimilarities[(i, j)]
                    bestTextNeighborhoodSim = textualNeighborhoodSimilarities[(i, j)]


        if bestWordSim>=ppdbSim and bestTextNeighborhoodSim>0 and [bestSourceIndex, bestTargetIndex] not in alignments:
            alignments.append([bestSourceIndex, bestTargetIndex])
            textualNeighborhoodAlignments.append([bestSourceIndex, bestTargetIndex])
            sourceWordIndicesAlreadyAligned.append(bestSourceIndex)
            targetWordIndicesAlreadyAligned.append(bestTargetIndex)
        
        if bestSourceIndex in sourceWordIndicesBeingConsidered:    
            sourceWordIndicesBeingConsidered.remove(bestSourceIndex)
        if bestTargetIndex in targetWordIndicesBeingConsidered:
            targetWordIndicesBeingConsidered.remove(bestTargetIndex)'''  


    alignments = [item for item in alignments if item[0]!=0 and item[1]!=0]
    sequenceAlignments = [item for item in sequenceAlignments if item[0]!=0 and item[1]!=0]
    namedEntityAlignments = [item for item in namedEntityAlignments if item[0]!=0 and item[1]!=0]
    dependencyNeighborhoodAlignments = [item for item in dependencyNeighborhoodAlignments if item[0]!=0 and item[1]!=0]
    textualNeighborhoodAlignments = [item for item in textualNeighborhoodAlignments if item[0]!=0 and item[1]!=0]
    noContextualOverlapAlignments = [item for item in noContextualOverlapAlignments if item[0]!=0 and item[1]!=0]
    
            
    return [alignments, sequenceAlignments, namedEntityAlignments, dependencyNeighborhoodAlignments, textualNeighborhoodAlignments, noContextualOverlapAlignments]
##############################################################################################################################







##############################################################################################################################
def align(sentence1, sentence2):


    sentence1Lemmatized = sentence1
    sentence2Lemmatized = sentence2

    

    sentence1LemmasAndPosTags = []
    for i in range(len(sentence1Lemmatized)):
        sentence1LemmasAndPosTags.append([])    
    for i in range(len(sentence1Lemmatized)):
        for item in sentence1Lemmatized[i]:
            sentence1LemmasAndPosTags[i].append(item)
        
 
    sentence2LemmasAndPosTags = []
    for i in range(len(sentence2Lemmatized)):
        sentence2LemmasAndPosTags.append([])    
    for i in range(len(sentence2Lemmatized)):
        for item in sentence2Lemmatized[i]:
            sentence2LemmasAndPosTags[i].append(item)
        

    myWordAlignments = alignWords(sentence1LemmasAndPosTags, 
                                  sentence2LemmasAndPosTags)

    #print [(sentence1LemmasAndPosTags[item[0]-1][2],
    #        sentence2LemmasAndPosTags[item[1]-1][2]) \
    #            for item in myWordAlignments[0] \
    #                if sentence1LemmasAndPosTags[item[0]-1][2].lower() not in \
    #                        stop_words+punctuations]

    #if newParseResults:
    #    pickle.dump(parse_results, open('sentence-parse-results.pickle', 'wb'))

    return myWordAlignments

    
##############################################################################################################################

sentence1 = "There is food at the dining hall."
sentence2 = "The dining hall serves food at 8 PM."
print(sentence1)
print(align(sentence1, sentence2))