NDLI: Generic soft pattern models for definitional question answering

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Generic soft pattern models for definitional question answering

Content Provider	ACM Digital Library
Author	Chua, Tat-Seng Kan, Min-Yen Cui, Hang
Abstract	This paper explores probabilistic lexico-syntactic pattern matching, also known as soft pattern matching. While previous methods in soft pattern matching are ad hoc in computing the degree of match, we propose two formal matching models: one based on bigrams and the other on the Profile Hidden Markov Model (PHMM). Both models provide a theoretically sound method to model pattern matching as a probabilistic process that generates token sequences. We demonstrate the effectiveness of these models on definition sentence retrieval for definitional question answering. We show that both models significantly outperform state-of-the-art manually constructed patterns. A critical difference between the two models is that the PHMM technique handles language variations more effectively but requires more training data to converge. We believe that both models can be extended to other areas where lexico-syntactic pattern matching can be applied.
Starting Page	384
Ending Page	391
Page Count	8
File Format	PDF
ISBN	1595930345
DOI	10.1145/1076034.1076101
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Probabilistic models Definitional question answering Soft pattern
Content Type	Text
Resource Type	Article

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