Lemmatizer for text in English. Inspired by Python's nltk.corpus.reader.wordnet.morphy package.
Based on code posted by mtbr at his blog entry WordNet-based lemmatizer
Version 0.2 has added functionality to add user supplied data at runtime
sudo gem install lemmatizer
require "lemmatizer"
lem = Lemmatizer.new
p lem.lemma("dogs", :noun ) # => "dog"
p lem.lemma("hired", :verb ) # => "hire"
p lem.lemma("hotter", :adj ) # => "hot"
p lem.lemma("better", :adv ) # => "well"
# when part-of-speech symbol is not specified as the second argument,
# lemmatizer tries :verb, :noun, :adj, and :adv one by one in this order.
p lem.lemma("fired") # => "fire"
p lem.lemma("slow") # => "slow"# Lemmatizer leaves alone words that its dictionary does not contain.
# This keeps proper names such as "James" intact.
p lem.lemma("MacBooks", :noun) # => "MacBooks"
# If an inflected form is included as a lemma in the word index,
# lemmatizer may not give an expected result.
p lem.lemma("higher", :adj) # => "higher" not "high"!
# The above has to happen because "higher" is itself an entry word listed in dict/index.adj .
# To fix this, modify the original dict directly (lib/dict/index.{noun|verb|adj|adv})
# or supply with custom dict files (recommended).# You can supply custom dict files consisting of lines in the format of <pos>\s+<form>\s+<lemma>.
# The data in user supplied files overrides the preset data. Here's the sample.
# --- sample.dict1.txt (don't include hash symbol on the left) ---
# adj higher high
# adj highest high
# noun MacBooks MacBook
# ---------------------------------------------------------------
lem = Lemmatizer.new("sample.dict1.txt")
p lem.lemma("higher", :adj) # => "high"
p lem.lemma("highest", :adj) # => "high"
p lem.lemma("MacBooks", :noun) # => "MacBook"
# The argument to Lemmatizer.new can be either of the following:
# 1) a path string to a dict file (e.g. "/path/to/dict.txt")
# 2) an array of paths to dict files (e.g. ["./dict/noun.txt", "./dict/verb.txt"])# You can use 'abbr' tag in user dicts to resolve abbreviations in text.
# --- sample.dict2.txt (don't include hash symbol on the left) ---
# abbr utexas University of Texas
# abbr mit Massachusetts Institute of Technology
# ---------------------------------------------------------------
# <NOTE>
# 1. Expressions on the right (substitutes) can contain white spaces,
# while expressions in the middle (words to be replaced) cannot.
# 2. Double/Single quotations could be used with substitute expressions,
# but not with original expressions.
lem = Lemmatizer.new("sample.dict2.txt")
p lem.lemma("utexas", :abbr) # => "University of Texas"
p lem.lemma("mit", :abbr) # => "Massachusetts Institute of Technology"- Yoichiro Hasebe yohasebe@gmail.com
Thanks for assistance and contributions:
- Vladimir Ivic http://vladimirivic.com
Licensed under the MIT license.