How to interpret doc2vec classifier in terms of words?

Question

I have trained a doc2vec (PV-DM) model in gensim on documents which fall into a few classes. I am working in a non-linguistic setting where both the number of documents and the number of unique words are small (~100 documents, ~100 words) for practical reasons. Each document has perhaps 10k tokens. My goal is to show that the doc2vec embeddings are more predictive of document class than simpler statistics and to explain which words (or perhaps word sequences, etc.) in each document are indicative of class.

I have good performance of a (cross-validated) classifier trained on the embeddings compared to one compared on the other statistic, but I am still unsure of how to connect the results of the classifier to any features of a given document. Is there a standard way to do this? My first inclination was to simply pass the co-learned word embeddings through the document classifier in order to see which words inhabited which classifier-partitioned regions of the embedding space. The document classes output on word embeddings are very consistent across cross validation splits, which is encouraging, although I don't know how to turn these effective labels into a statement to the effect of "Document X got label Y because of such and such properties of words A, B and C in the document".

Another idea is to look at similarities between word vectors and document vectors. The ordering of similar word vectors is pretty stable across random seeds and hyperparameters, but the output of this sort of labeling does not correspond at all to the output from the previous method.

Thanks for help in advance.

Edit: Here are some clarifying points. The tokens in the "documents" are ordered, and they are measured from a discrete-valued process whose states, I suspect, get their "meaning" from context in the sequence, much like words. There are only a handful of classes, usually between 3 and 5. The documents are given unique tags and the classes are not used for learning the embedding. The embeddings have rather dimension, always < 100, which are learned over many epochs, since I am only worried about overfitting when the classifier is learned, not the embeddings. For now, I'm using a multinomial logistic regressor for classification, but I'm not married to it. On that note, I've also tried using the normalized regressor coefficients as vector in the embedding space to which I can compare words, documents, etc.

Answer 1

That's a very small dataset (100 docs) and vocabulary (100 words) compared to much published work of Doc2Vec, which has usually used tens-of-thousands or millions of distinct documents.

That each doc is thousands of words and you're using PV-DM mode that mixes both doc-to-word and word-to-word contexts for training helps a bit. I'd still expect you might need to use a smaller-than-defualt dimensionaity (vector_size<<100), & more training epochs - but if it does seem to be working for you, great.

You don't mention how many classes you have, nor what classifier algorithm you're using, nor whether known classes are being mixed into the (often unsupervised) Doc2Vec training mode.

If you're only using known classes as the doc-tags, and your "a few" classes is, say, only 3, then to some extent you only have 3 unique "documents", which you're training on in fragments. Using only "a few" unique doctags might be prematurely hiding variety on the data that could be useful to a downstream classifier.

On the other hand, if you're giving each doc a unique ID - the original 'Paragraph Vectors' paper approach, and then you're feeding those to a downstream classifier, that can be OK alone, but may also benefit from adding the known-classes as extra tags, in addition to the per-doc IDs. (And perhaps if you have many classes, those may be OK as the only doc-tags. It can be worth comparing each approach.)

I haven't seen specific work on making Doc2Vec models explainable, other than the observation that when you are using a mode which co-trains both doc- and word- vectors, the doc-vectors & word-vectors have the same sort of useful similarities/neighborhoods/orientations as word-vectors alone tend to have.

You could simply try creating synthetic documents, or tampering with real documents' words via targeted removal/addition of candidate words, or blended mixes of documents with strong/correct classifier predictions, to see how much that changes either (a) their doc-vector, & the nearest other doc-vectors or class-vectors; or (b) the predictions/relative-confidences of any downstream classifier.

(A wishlist feature for Doc2Vec for a while has been to synthesize a pseudo-document from a doc-vector. See this issue for details, including a link to one partial implementation. While the mere ranked list of such words would be nonsense in natural language, it might give doc-vectors a certain "vividness".)

Whn you're not using real natural language, some useful things to keep in mind:

• if your 'texts' are really unordered bags-of-tokens, then window may not really be an interesting parameter. Setting it to a very-large number can make sense (to essentially put all words in each others' windows), but may not be practical/appropriate given your large docs. Or, trying PV-DBOW instead - potentially even mixing known-classes & word-tokens in either tags or words.
• the default ns_exponent=0.75 is inherited from word2vec & natural-language corpora, & at least one research paper (linked from the class documentation) suggests that for other applications, especially recommender systems, very different values may help.