Semantic search with NLP and elasticsearch

Question

I am experimenting with elasticsearch as a search server and my task is to build a \"semantic\" search functionality. From a short text phrase like \"I have a burst pipe\" t

Answer 1

You may want to explore https://blog.conceptnet.io/2016/11/03/conceptnet-5-5-and-conceptnet-io/.

It combines semantic networks and distributional semantics.

When most developers need word embeddings, the first and possibly only place they look is word2vec, a neural net algorithm from Google that computes word embeddings from distributional semantics. That is, it learns to predict words in a sentence from the other words around them, and the embeddings are the representation of words that make the best predictions. But even after terabytes of text, there are aspects of word meanings that you just won’t learn from distributional semantics alone.

Some results

The ConceptNet Numberbatch word embeddings, built into ConceptNet 5.5, solve these SAT analogies better than any previous system. It gets 56.4% of the questions correct. The best comparable previous system, Turney’s SuperSim (2013), got 54.8%. And we’re getting ever closer to “human-level” performance on SAT analogies — while particularly smart humans can of course get a lot more questions right, the average college applicant gets 57.0%.