Diversified Query Expansion aims to present the user with a diverse list of query expansions so as to better communicate their intent to the retrieval system. Current diversified expansion techniques either make use of external knowledge sources to explicitly model the various aspects and their relationships underlying the user query or implicitly model query aspects. However these techniques assume query aspects to be independent of each other. We propose a unified framework that produces diversified query expansions in a completely implicit manner while also considering the relationships between query aspects. In particular, the framework identifies query aspects and their relationships by making use of the semantic properties of context phrases that occur within the top-ranked retrieved documents for the supplied user query, and maps them onto a Mutating Markov Chain model to generate a diverse ordering of query aspects. We test our framework against a set of ambiguous and faceted queries used in the NTCIR-12 IMine-2 Task and through an extensive empirical analysis, we show that our framework consistently outperforms existing implicit diversified query expansion algorithms. The utility of our algorithm truly comes up in the second set of experiments where we generate diversified query expansions for a retrieval engine indexing documents from specific scientific domains. Even in such a niche scenario our algorithm consistently provides robust results and performs better than other implicit approaches
R. E. Dev and Dr. Vidhya Balasubramanian, “An Implicit Aspect Modelling Framework for Diversity Focused Query Expansion”, Journal of Intelligent Information Systems, 2019.