Applications of machine learning and rule induction
Communications of the ACM
Mixed-initiative development of language processing systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
HITIQA: a data driven approach to interactive analytical question answering
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Quality-driven query answering for integrated information systems
Quality-driven query answering for integrated information systems
Experience of using GATE for NLP R&D
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems
| Hi-index | 0.00 |
The authors report on a series of experiments to automate the assessment of document qualities such as depth and objectivity. The primary purpose is to develop a quality-sensitive functionality, orthogonal to relevance, to select documents for an interactive question-answering system. The study consisted of two stages. In the classifier construction stage, nine document qualities deemed important by information professionals were identified and classifiers were developed to predict their values. In the confirmative evaluation stage, the performance of the developed methods was checked using a different document collection. The quality prediction methods worked well in the second stage. The results strongly suggest that the best way to predict document qualities automatically is to construct classifiers on a person-by-person basis. © 2006 Wiley Periodicals, Inc.