Document embedding is often considered an underrated way of doing things. The key idea in document embedding is to compress an entire document, for example a patent or customer review, into one single vector. This vector in turn can be used for a lot of downstream tasks.
Empirical results show that document vectors outperform bag-of-words models as well as other techniques for text representation.
Among the most useful downstream tasks is the ability to cluster text. Text clustering has several uses, ranging from data exploration to online classification of incoming text in a pipeline.
In particular, we are interested in document modeling using doc2vec on a small dataset. Unlike sequence models such as RNN, where a word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent. This word order independence means...
