有没有一种方法可以在训练doc2vec模型之前加载预训练的词向量？

Question

I am trying to build a doc2vec model with more or less 10k sentences, after that I will use the model to find the most similar sentence in the model of some new sentences.

I have trained a gensim doc2vec model using the corpus(10k sentences) I have. This model can to some extend tell me if a new sentence is similar to some of the sentences in the corpus. But, there is a problem: it may happen that there are words in new sentences which don't exist in the corpus, which means that they don't have a word embedding. If this happens, the prediction result will not be good. As far as I know, the trained doc2vec model does have a matrix of doc vectors as well as a matrix of word vectors. So what I were thinking is to load a set of pre-trained word vectors, which contains a large number of words, and then train the model to get the doc vectors. Does it make sense? Is it possible with gensim? Or is there another way to do it?

Answer 1

Unlike what you might guess, typical Doc2Vec training does not train up word-vectors first, then compose doc-vectors using those word-vectors. Rather, in the modes that use word-vectors, the word-vectors trained in a simultaneous, interleaved fashion alongside the doc-vectors, both changing together. And in one fast and well-performing mode, PV-DBOW ( dm=0 in gensim), word-vectors aren't trained or used at all.

So, gensim Doc2Vec doesn't support pre-loading state from elsewhere, and even if it did, it probably wouldn't provide the benefit you expect. (You could dig through the source code & perhaps force it by doing a bunch of initialization steps yourself. But then, if words were in the pre-loaded set, but not in your training data, training the rest of the active words would adjust the entire model in direction incompatible with the imported-but-untrained 'foreign' words. It's only the interleaved, tug-of-war co-training of the model's state which makes the various vectors meaningful in relation to each other.)

The most straightforward and reliable strategy would be to try to expand your training corpus, by finding more documents from a similar/compatible domain, to include multiple varied examples of any words you might encounter later. (If you thought some other word-vectors were apt enough for your domain, perhaps the texts that were used to train those word-vectors can be mixed-into your training corpus. That's a reasonable way to put the word/document data from that other source on equal footing in your model.)

And, as new documents arrive, you can also occasionally re-train the model from scratch, with the now-expanded corpus, letting newer documents contribute equally to the model's vocabulary and modeling strength.