A Hybrid Approach for QA Track Definitional Questions

This paper presents an overview of DefScriber, a hybrid goal-driven and data-driven system for definitional questions that was developed at Columbia University. DefScriber combines knowledge-based and statistical methods to answer definitional questions of the form, What is X? The authors explain ho...

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Hauptverfasser:	Blair-Goldensohn, Sasha, McKeown, Kathleen R, Schlaikjer, Andrew H
Format:	Report
Sprache:	eng
Schlagworte:	ALGORITHMS ARTIFICIAL INTELLIGENCE CLASSIFICATION CLUSTERING COHESION ORDERING COMPUTATIONAL LINGUISTICS Computer Programming and Software COMPUTER PROGRAMS Cybernetics DATA BASES DEFINITIONAL PREDICATES DEFINITIONAL QUESTIONS EXTRACTION HYBRID SYSTEMS INDEX TERMS INFORMATION RETRIEVAL Information Science INTERNET LEARNING MACHINES Linguistics MACHINE LEARNING MATCHING MULTI-SENTENCE ANSWERS NSD(NON-SPECIFIC DEFINITIONAL) PATTERN RECOGNITION PERFORMANCE(ENGINEERING) PREDICATE IDENTIFICATION QUESTION AND ANSWER RIPPER ALGORITHM SEARCH QUERIES SEARCHING SENTENCE CLUSTERING SENTENCE SELECTION SOFTWARE ARCHITECTURE STATISTICAL ANALYSIS SUMMARIZATION SYNTAX SYNTAX-TREE PATTERNS
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Zusammenfassung:	This paper presents an overview of DefScriber, a hybrid goal-driven and data-driven system for definitional questions that was developed at Columbia University. DefScriber combines knowledge-based and statistical methods to answer definitional questions of the form, What is X? The authors explain how the system was modified and applied to answer definitional questions in the TREC 2003 QA track. They present DefScriber's results on the definitional questions, which were significantly above median, achieving an average F-score of .338 compared with the median of .192. Finally, they analyze their scores on certain individual questions, discussing areas in which their system performed well and areas in which it could be improved. The original document contains color images.