David R. Pierce

Multidocument summarization via information extraction

We present and evaluate the initial version of RIPTIDES, a system that combines information extraction, extraction-based summarization, and natural language generation to support user-directed multidocument summarization.

Examining the Role of Statistical and Linguistic Knowledge Sources in a General-Knowledge Question-Answering System

We describe and evaluate an implemented system for general-knowledge question answering. The system combines techniques for standard ad-hoc information retrieval (IR), query-dependent text summarization, and shallow syntactic and semantic sentence analysis. In a series of experiments we examine the role of each statistical and linguistic knowledge source in the question-answering system. In contrast to previous results, we find first that statistical knowledge of word co-occurrences as computed by IR vector space methods can be used to quickly and accurately locate the relevant documents for each question. The use of query-dependent text summarization techniques, however, provides only small increases in performance and severely limits recall levels when inaccurate. Nevertheless, it is the text summarization component that allows subsequent linguistic filters to focus on relevant passages. We find that even very weak linguistic knowledge can offer substantial improvements over purely IRbased techniques for question answering, especially when smoothly integrated with statistical preferences computed by the IR subsystems.

The Smart/Empire TIPSTER IR System

The primary goal of the Cornell/Sabir TIPSTER Phase III project is to develop techniques to improve the end-user efficiency of information retrieval (IR) systems. We have focused our investigations in four related research areas:1. High Precision Information Retrieval. The goal of our research in this area is to increase the accuracy of the set of documents given to the user.