This resource allows for matching of Turkish words or expressions with their corresponding entries within the Turkish dictionary, the Turkish PropBank TRopBank, morphological analysis, named entity recognition, word senses from Turkish WordNet KeNet, shallow parsing, and universal dependency relation.
The structure of a sample annotated word is as follows:
{turkish=Gelir}
{morphologicalAnalysis=gelir+NOUN+A3SG+PNON+NOM}
{metaMorphemes=gelir}
{semantics=TUR10-0289950}
{namedEntity=NONE}
{propbank=ARG0$TUR10-0798130}
{shallowParse=ÖZNE}
{universalDependency=10$NSUBJ}
As is self-explanatory, 'turkish' tag shows the original Turkish word; 'morphologicalAnalysis' tag shows the correct morphological parse of that word; 'semantics' tag shows the ID of the correct sense of that word; 'namedEntity' tag shows the named entity tag of that word; 'shallowParse' tag shows the semantic role of that word; 'universalDependency' tag shows the index of the head word and the universal dependency for this word; 'propbank' tag shows the semantic role of that word for the verb synset id (frame id in the frame file) which is also given in that tag.
You can also see Cython, Java, C++, C, Swift, Js, or C# repository.
To check if you have a compatible version of Python installed, use the following command:
python -V
You can find the latest version of Python here.
Install the latest version of Git.
pip3 install NlpToolkit-AnnotatedSentence
In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:
git clone <your-fork-git-link>
A directory called AnnotatedSentence will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/AnnotatedSentence-Py.git
Steps for opening the cloned project:
- Start IDE
- Select File | Open from main menu
- Choose
AnnotatedSentence-Pyfile - Select open as project option
- Couple of seconds, dependencies will be downloaded.
To load the annotated corpus:
AnnotatedCorpus(self, folder: str, pattern: str = None)
a = AnnotatedCorpus("/Turkish-Phrase", ".train")
b = AnnotatedCorpus("/Turkish-Phrase")
To access all the sentences in a AnnotatedCorpus:
for i in range(a.sentenceCount()):
annotatedSentence = a.getSentence(i)
....
Bir AnnotatedSentence'daki tüm kelimelere ulaşmak için de
for j in range(annotatedSentence.wordCount()):
annotatedWord = annotatedSentence.getWord(j)
...
An annotated word is kept in AnnotatedWord class. To access the morphological analysis of the annotated word:
getParse(self) -> MorphologicalParse
Meaning of the annotated word:
getSemantic(self) -> str
NER annotation of the annotated word:
getNamedEntityType(self) -> NamedEntityType
Shallow parse tag of the annotated word (e.g., subject, indirect object):
getShallowParse(self) -> str
Dependency annotation of the annotated word:
getUniversalDependency(self) -> UniversalDependencyRelation
@INPROCEEDINGS{8374369,
author={O. T. {Yıldız} and K. {Ak} and G. {Ercan} and O. {Topsakal} and C. {Asmazoğlu}},
booktitle={2018 2nd International Conference on Natural Language and Speech Processing (ICNLSP)},
title={A multilayer annotated corpus for Turkish},
year={2018},
volume={},
number={},
pages={1-6},
doi={10.1109/ICNLSP.2018.8374369}}