How can we use artificial neural networks to find similar documents?

Question

How can we use ANN to find some similar documents? I know its a silly question, but I am new to this NLP field. I have made a model using kNN and bag-of-words approach to solve my problem. Using that I can get n number of documents (along with their closeness) that are somewhat similar to the input, but now I want to implement the same using ANN and I am not getting any idea.

Thanks in advance for any help or suggestions.

Answer 1

You can use "word embeddings" - technique, that presents words in the dense vector representation. To find similar documents as the vectors, you can simply use cosine similarity .

An example how to build word2vec model using TensorFlow. One more example how to use embeddings layer from Keras.

Answer 2

The way to obtain embeddings for your language is either training them yourself on your corpus of choice (large enough - eg wikipedia) or downloading the trained embeddings (for python there are plenty of sources for embeddings trained or loadable with gensim module - which is a de facto standard for Python word2vec).

You can also use GloVe (using glove-python ) or FastText word embeddings.

If you are interested you can find more detailed descriptions of embeddings with code examples and source papers .

Answer 3

Have a look at the paper https://arxiv.org/pdf/1805.10685.pdf that gives you a overall idea. check this link for more references https://github.com/Hironsan/awesome-embedding-models