Paper: Improving the Performance of the Random Walk Model for Answering Complex Questions

ACL ID P08-2003
Title Improving the Performance of the Random Walk Model for Answering Complex Questions
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We consider the problem of answering com- plex questions that require inferencing and synthesizing information from multiple doc- uments and can be seen as a kind of topic- oriented, informative multi-document summa- rization. The stochastic, graph-based method for computing the relative importance of tex- tual units (i.e. sentences) is very successful in generic summarization. In this method, a sentence is encoded as a vector in which each component represents the occurrence fre- quency (TF*IDF) of a word. However, the major limitation of the TF*IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. In this pa- per, we study the impact of syntactic and shal- low semantic information in the graph-base...