Efficient question answering with question decomposition and multiple answer streams

  • Authors:

  • Sven Hartrumpf;Ingo Glöckner;Johannes Leveling

  • Affiliations:

  • Intelligent Information and Communication Systems, University of Hagen, Hagen, Germany;Intelligent Information and Communication Systems, University of Hagen, Hagen, Germany;Centre for Next Generation Localisation, Dublin City University, Dublin 9, Ireland

  • Venue:
  • CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

The German question answering (QA) system IRSAW (formerly: InSicht) participated in QA@CLEF for the fifth time. IRSAW was introduced in 2007 by integrating the deep answer producer InSicht, several shallow answer producers, and a logical validator. InSicht builds on a deep QA approach: it transforms documents to semantic representations using a parser, draws inferences on semantic representations with rules, and matches semantic representations derived from questions and documents. InSicht was improved for QA@CLEF 2008 mainly in the following two areas. The coreference resolver was trained on question series instead of newspaper texts in order to be better applicable for follow-up questions. Questions are decomposed by several methods on the level of semantic representations. On the shallow processing side, the number of answer producers was increased from two to four by adding FACT, a fact index, and SHASE, a shallow semantic network matcher. The answer validator introduced in 2007 was replaced by the faster RAVE validator designed for logic-based answer validation under time constraints. Using RAVE for merging the results of the answer producers, monolingual German runs and bilingual runs with source language English and Spanish were produced by applying the machine translation web service Promt. An error analysis shows the main problems for the precision-oriented deep answer producer InSicht and the potential offered by the recalloriented shallow answer producers.