semantic-kit 0.0.3

A toolkit to estimate semantic similarity or relatedness

Semantic Similarity and Relatedness Toolkit

A toolkit to estimate semantic similarity and relatedness between two words/sentences.

Installation

pip install semantic-kit

Functions

1. Lesk algorithm and improved version
2. Similarity algorithms including WordNet , word2vec similarity, LDA, and googlenews-based methods
3. Distance algorithms like jaccard, soren, levenshtein, and their improved versions
4. Use Open Multilingual Wordnet to generate relevant keywords from multiple language

Examples

Lesk Algorithm

from semantickit.relatedness.lesk import lesk
from semantickit.relatedness.lesk_max_overlap import lesk_max_overlap
sent = ['I', 'went', 'to', 'the', 'bank', 'to', 'deposit', 'money', '.']
m1, s1 = lesk(sent, 'bank', 'n')
m2, s2 = lesk_max_overlap(sent, 'bank', 'n')
print(m1,s1)
print(m2,s2)

WordNet-based Similarity

from semantickit.similarity.wordnet_similarity import wordnet_similarity_all
print(wordnet_similarity_all("dog.n.1","cat.n.1"))

Corpus-based Similarity

from semantickit.similarity.word2vec_similarity import build_model,similarity_model
# build similarity model based on text source
build_model(data_path="text8",save_path="wiki_model")
# estimate similarity between words using the built model
sim=similarity_model("wiki_model","france","spain")
# print result
print("word2vec similarity: ",sim)

Pre-trained model-based Similarity

from semantickit.similarity.googlenews_similarity import googlenews_similarity
data_path= r'GoogleNews-vectors-negative300.bin'
sim=googlenews_similarity(data_path,'human','people')
print(sim)

Weighted Levenshtein

from semantickit.distance.n_gram.train_ngram import TrainNgram
from semantickit.distance.weighted_levenshtein import weighted_levenshtein,Build_TFIDF

# train model
train_data_path = 'wlev/icd10_train.txt'
wordict_path = 'wlev/word_dict.model'
transdict_path = 'wlev/trans_dict.model'
words_path="wlev/dict_words.txt"
trainer = TrainNgram()
trainer.train(train_data_path, wordict_path, transdict_path)

# build words tf-idf file
Build_TFIDF(train_data_path,words_path)

# estimate weight lev distance
s0='颈结缔组织良性肿瘤'
s1='耳软骨良性肿瘤'
result=weighted_levenshtein(s0,s1, word_dict_path=wordict_path,trans_dict_path=transdict_path,data_path=train_data_path,words_path=words_path)
print(result)

Get related words

# use wordnet to generate multi-lang keywords
from semantickit.lang.wordnet import *
if __name__=="__main__":
    text = "digitalization meets carbon neutrality, digital economy"
    nltk.download("wordnet")
    nltk.download('omw')
    dict_lang_all=get_all_related_word_from_text(text)
    print()
    for lang in dict_lang_all:
        print(lang, ','.join(dict_lang_all[lang]))

Chinese WordVector Similarity

from semantickit.similarity.chinese_word2vec_similarity import ChineseWord2Vec
cwv=ChineseWord2Vec(
    data_path="data/source",
    output_segment_path="data/segment",
    stop_words_path="data/stop_words.txt",
    user_dict_path="data/user_dict.txt",
    word2vec_model_path="models/word2Vec.model"
)
cwv.train()
sim=cwv.similarity('沙瑞金','易学习')
print(sim)

License

The Semantic-Kit project is provided by Donghua Chen.