Web search engines help users find relevant web pages by returning a result set containing the pages that best match the user’s query. When the identified pages have low relevance, the query must be refined to capture the search goal more effectively. However, finding appropriate refinement terms is difficult and time consuming for users, so researchers developed query expansion approaches to identify refinement terms automatically. There are two broad approaches to query expansion, automatic query expansion (AQE) and interactive query expansion (IQE) (Ruthven et al., 2003). AQE has no user involvement, which is simpler for the user, but limits its performance. IQE has user involvement, which is more complex for the user, but means it can tackle more problems such as ambiguous queries. Searches fail by finding too many irrelevant pages (low precision) or by finding too few relevant pages (low recall). AQE has a long history in the field of information retrieval, where the focus has been on improving recall (Velez et al., 1997). Unfortunately, AQE often decreased precision as the terms used to expand a query often changed the query’s meaning (Croft and Harper (1979) identified this effect and named it query drift). The problem is that users typically consider just the first few results (Jansen et al., 2005), which makes precision vital to web search performance. In contrast, IQE has historically balanced precision and recall, leading to an earlier uptake within web search. However, like AQE, the precision of IQE approaches needs improvement. Most recently, approaches have started to improve precision by incorporating semantic knowledge.
Combining Language Models with NLP and Interactive Query Expansion