Pseudo-Query Reformulation
This work addresses query reformulation for information retrieval systems, but it appears incremental as it builds on existing performance prediction methods within a new framework.
The paper tackles the problem of improving retrieval result ranking by automatically rewriting user queries, presenting a discrete optimization framework that treats query reformulation as a search over a graph of queries linked by minimal transformations, and demonstrates its effectiveness on public datasets.
Automatic query reformulation refers to rewriting a user's original query in order to improve the ranking of retrieval results compared to the original query. We present a general framework for automatic query reformulation based on discrete optimization. Our approach, referred to as pseudo-query reformulation, treats automatic query reformulation as a search problem over the graph of unweighted queries linked by minimal transformations (e.g. term additions, deletions). This framework allows us to test existing performance prediction methods as heuristics for the graph search process. We demonstrate the effectiveness of the approach on several publicly available datasets.