Word2Vec and Pseudo Relevance Feedback for Elasticsearch in IR
Pseudo Feedback for Elasticsearch in Information Retrieval.
This work focuses on query expansion for first step, and pseudo feedback for second.
For query expansion, I experiments on both locally trained word embedding (MT bi-lingual English source) and FastText pre-trained Wiki word embedding. The results get better when using pre-trained embedding.
For retrieval part, I also indexed and searched MT and GOLD (manually) translated data. Though vocabulary matches for MT data, GOLD works better, with 3% better of miss probabillity.
Further experiments will be conducted for Pseudo Feedback.