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I am currently working on word2vec model using gensim in Python, and want to write a function that can help me find the antonyms and synonyms of a given word. For example: antonym("sad")="happy" synonym("upset")="enraged"
Is there a way to do that in word2vec?
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let we say there is a word X and its synonym Y. and also have antonym of Y which is Z. then we can say X-Y + Z = antonym of (X) and synonym of(Z). use model.
The word2vec algorithms include skip-gram and CBOW models, using either hierarchical softmax or negative sampling: Tomas Mikolov et al: Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov et al: Distributed Representations of Words and Phrases and their Compositionality.
Word2vec (Skipgram) At a high level Word2Vec is a unsupervised learning algorithm that uses a shallow neural network (with one hidden layer) to learn the vectorial representations of all the unique words/phrases for a given corpus.
It represents words or phrases in vector space with several dimensions. Word embeddings can be generated using various methods like neural networks, co-occurrence matrix, probabilistic models, etc. Word2Vec consists of models for generating word embedding.
In word2vec you can find analogies, the following way
model = gensim.models.Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
model.most_similar(positive=['good', 'sad'], negative=['bad'])
[(u'wonderful', 0.6414928436279297),
(u'happy', 0.6154338121414185),
(u'great', 0.5803680419921875),
(u'nice', 0.5683973431587219),
(u'saddening', 0.5588893294334412),
(u'bittersweet', 0.5544661283493042),
(u'glad', 0.5512036681175232),
(u'fantastic', 0.5471092462539673),
(u'proud', 0.530515193939209),
(u'saddened', 0.5293528437614441)]
Now using some standard antonyms like (good, bad), (rich, poor), find multiple such lists of nearest antonyms. After that you can use average of vectors of this list.
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