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Title: Soft matching for question answering
Authors: CUI HANG
Keywords: soft pattern, question answering, relation matching
Issue Date: 19-Sep-2006
Citation: CUI HANG (2006-09-19). Soft matching for question answering. ScholarBank@NUS Repository.
Abstract: I identify weaknesses in exact matching of syntactic and semantic features in current question answering (QA) systems. Such hard matching may fare poorly given variations in natural language texts. To combat such problems, I develop two soft matching schemes. I implement both soft matching schemes using statistical models and apply them to two components in a QA system. Such a QA system is designed to fulfill the information need of advanced users who search for information in a systematic way. Taking a search target as input, the QA system can produce a summarized profile, or definition, for the target and answer a series of factoid questions about the target. To build up the QA system, I develop two key components -- (1) the definitional question answering system that generates the definition for a given target; and (2) the factoid question answering system that is responsible for answering specific questions. In this thesis, I focus on precise sentence retrieval for these two components and evaluate them component-wise. To retrieve definition sentences that construct the definition, I apply lexico-syntactic pattern matching to identify definition sentences. Most current systems employ hard matching of manually constructed definition patterns, which may have the problem of low recall due to language variations. To combat this problem, I adopt the soft matching scheme anchored at the search target. In particular, I develop three soft pattern models -- a simple baseline model and two formal ones based on the bigram model and the Profile Hidden Markov Model (PHMM), respectively. The soft pattern models generalize pattern matching as the process of producing token sequences. I experimentally show that employing soft pattern models greatly outperforms the system that utilizes hard matching of pattern rules. To obtain precise answer sentences for a specific factoid question about a target, I examine the dependency relations between matched question words in addition to lexical matching. As the same relations may be phrased differently, I adopt another soft matching scheme. Specifically, I employ a machine translation model to implement this soft matching scheme to compute the similarity between multiple relations. I experimentally demonstrate that the passage retrieval performance is significantly augmented by combining soft relation matching with lexical matching. The main contribution of this thesis is in developing soft matching schemes to flexibly match both lexico-syntactic patterns and dependency relations, and applying the soft matching models to sentence retrieval for answering definition and factoid questions.
Appears in Collections:Ph.D Theses (Open)

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