Paper: Selecting Sentences for Answering Complex Questions

ACL ID D08-1032
Title Selecting Sentences for Answering Complex Questions
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic- oriented, informative multi-document summa- rization. In this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: k- means and Expectation Maximization (EM), for computing relative importance of the sen- tences. However, the performance of these ap- proaches depends entirely on the feature set used and the weighting of these features. We extracted different kinds of features (i.e. lex- ical, lexical semantic, cosine similarity, ba- sic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences in order to measure its importance and relevancy to the user query. We used a loc...