Get most similar words, given the vector of the word (not the word itself)
Using the gensim.models.Word2Vec library, you have the possibility to provide a model and a "word" for which you want to find the list of most similar words:
model = gensim.models.Word2Vec.load_word2vec_format(model_file, binary=True)
model.most_similar(positive=[WORD], topn=N)
I wonder if there is a possibility to give the system as input the model and a "vector", and ask the system to return the top similar words (which their vectors is very close to the given vector). Something similar to:
model.most_similar(positive=[VECTOR], topn=N)
I need this functionality for a bilingual setting, in which I have 2 models (English and German), as well as some English words for which I need to find their most similar German candidates. What I want to do is to get the vector of each English word from the English model:
model_EN = gensim.models.Word2Vec.load_word2vec_format(model_file_EN, binary=True)
vector_w_en=model_EN[WORD_EN]
and then query the German model with these vectors.
model_DE = gensim.models.Word2Vec.load_word2vec_format(model_file_DE, binary=True)
model_DE.most_similar(positive=[vector_w_en], topn=N)
I have implemented this in C using the original distance function in the word2vec package. But, now I need it to be in python, in order to be able to integrate it with my other scripts.
Do you know if there is already a method in gensim.models.Word2Vec library or other similar libraries which does this? Do I need to implement it by myself?
Answer
The method similar_by_vector returns the top-N most similar words by vector:
similar_by_vector(vector, topn=10, restrict_vocab=None)