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In Genism's doc2vec,context is a list of words passed to models. That is a hyperparameter selected by the user, thus will problem specific.


It might depend on what you will then use the scores for. For instance, should one long sentence score higher than two shorter sentences even if all three sentences have the same density of technical words? If so, adding rather than averaging the scores? Or adding, then doing an adjustment for sentence length. The other way to get the more technical words to ...


Unfortunately, there is little theoretical knowledge about what complex neural networks do. Transformers are known to be universal approximations, so in theory they can learn to do any function with the input sentence, unlike the other alternatives that you mention. Most of the time, the accuracy of the BERT-like model would be strictly better. In practice, ...

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