People browsing the web or reading a document may see text passages that describe a topic of interest, and want to know more about it by searching. Manually formulating a query from that text can be difficult, however, and an effective search is not guaranteed. In this paper, to address this scenario, we propose a learning-based approach which generates effective queries from the content of an arbitrary userselected text passage. Specifically, the approach extracts and selects representative chunks (noun phrases or named entities) from the content (a text passage) using a rich set of features. We carry out experiments showing that the selected chunks can be effectively used to generate queries both in a TREC environment, where weights and query structure can be directly incorporated, and with a "black-box" web search engine, where query structure is more limited.
Lee and Croft REF generate queries from textual passages by extracting noun phrases or named entities; a conditional random field is used to find selections that optimize retrieval effectiveness.
Generating queries from user-selected text
IIiX
Computer Science