Calculate semantic similarity of each pair of sentences/paragraphs in two texts (English or Chinese).
Models include word2vec, tfidf, lda, lsi.
Python 3.6.5
pandas, numpy, jieba, nltk, gensim, re, sklearn, codecs, time
Since semantic similarity labeling is difficult and time consuming, unsupervised semantic similarity caculating is useful.
I choose four models to calculate semantic vectors of texts, then utilize cosine distance to calculate similarity.
Prepare your data: pairs of sentence/paragraphs in two txt files
Command: excute.py text1_path text2_path res_path -l en/cn
You can run excute.py with -h to get information about arguments details.
I implement four models to calculate unsupervised semantic similarity including word2vec, tfidf, lda, lsi.
By changing setting in config.py:
You can change the output vector dimension of each model except tfidf (dimension depends on corpus size).
You can combine more than one models to generate semantic vectors of texts.
As a beginner interested in NLP/Data Mining, I would be delighted for your encouragement!
Feel free to mail me at 953383269@qq.com with any comments/problems/questions/suggestions.