gensim function predict output words

I use the gensim library to create a word2vec model. It contains the function predict_output_words() which I understand as follows:

predict_output_words()

For example, I have a model that is trained with the sentence: "Anarchism does not offer a fixed body of doctrine from a single particular world view instead fluxing and flowing as a philosophy."

and then I use

model.predict_output_words(context_words_list=['Anarchism', 'does', 'not', 'offer', 'a', 'fixed', 'body', 'of', 'from', 'a', 'single', 'particular', 'world', 'view', 'instead', 'fluxing'], topn=10).

model.predict_output_words(context_words_list=['Anarchism', 'does', 'not', 'offer', 'a', 'fixed', 'body', 'of', 'from', 'a', 'single', 'particular', 'world', 'view', 'instead', 'fluxing'], topn=10)

In this situation, could I get/predict the correct word or the omitted word 'doctrine'?

Is this the right way? Please explain this function in detail.

1 Answer
1

I am wondering if you have seen the documentation of predict_output_word?

predict_output_word

Report the probability distribution of the center word given the
context words as input to the trained model.

To answer your specific question about the word 'doctrine' - it strongly depends if for the words you listed as your context one of the 10 most probable words is 'doctrine'. This means that it must occur relatively frequently in the corpus you use for training of the model. Also, since 'doctrine' does not seem to be one of the very often used words there is a high chance other words will have a higher probability of appearing in the context. Therefore, if you base only on the returned probability of the words given the context you may end up failing to predict 'doctrine' in this case.

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