Xerox TREC-8 Question Answering Track Report.

Abstract

This report describes the Xerox work on the TREC-8 Question Answering Track. We linked together a few basic NLP components (a question parser, a sentence boundary identifier, and a proper noun tagger) with a sentence scoring function and an answer presentation function built specifically for the TREC Q&A task. Our system found the correct 50-byte answer (in the top 5 responses) to 45% of the questions, a quite respectable performance, but with considerable room for improvement. Based on the failure analysis presented in this paper, we can conclude that the system would benefit from having access to a broad range of other NLP technologies, including robust parsing and coreference analysis, or some good heuristic approximations thereof. The system also has a clear need for some semantic resources to help with certain difficult problems, such as finding answers that match the semantic class X in What X? questions. 1 Introduction Natural language processing (NLP) techniques have...