Explicit Suggestion of Query Terms for News Search using Topic Models and Word Embeddings

This report presents a study on assisting users in building queries to perform real-time searches in a news and social media monitoring system. The system accepts complex queries, and we assist the user by suggesting related keywords or entities. We do this by leveraging two different word representations: (1) probabilistic topic models, and (2) unsupervised word embeddings. We compare the vector representations obtained by these two approaches to find related keywords (i.e. suggestions) with respect to specific queries, taken from the query log of a commercial system. Through crowdsourcing we solicited relevance judgments and compared the two methods. Our results show that word embeddings outperform topic models for keyword suggestion.