This project attempt to build a query-rewriter, which converts the raw queries into clean and in-vocab text. It is a preparation for feeding the COM-AID model.

IF-IDF is a comparatively light weighting schedule to find the near concepts of a specific given query. However, it is based on a prerequisite that all the words in the text should be in the vocalbuary. To address this problem, we can replace the out-of-vocab words with in-vocab words using KNN.

Brute force is too time-consuming in finding the top K nearest neighbors. We build a ball tree to organize the data set(word embeddings) instead of simple list. A O(lgn) algorithm based on tree structure would be better than O(n) based on BF.

If words in the query has no same words(totally different) with the corresponding concepts, TF-IDF can hard to find it in the candidates.

Running TF-IDF.py is just ok.