Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA

Proceedings of the 43rd Annual Meeting of the ACL, pages 215–222, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA Yutaka Sasaki Department of Natural Language Processing ATR Spoken Language Communication Research Laboratories 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288 Japan yutaka.sasaki@atr.jp Abstract This paper regards Question Answering (QA) as Question-Biased Term Extraction (QBTE). This new QBTE approach lib- erates QA systems from the heavy bur- den imposed by question types (or answer types). In conventional approaches, a QA system analyzes a given question and de- termines the question type, and then it se- lects answers from among answer candi- dates that match the question type. Con- sequently, the output of a QA system is restricted by the design of the question types. The QBTE directly extracts an- swers as terms biased by the question. To confirm the feasibility of our QBTE ap- proach, we conducted experiments on the CRL QA Data based on 10-fold cross val- idation, using Maximum Entropy Models (MEMs) as an ML technique. Experimen- tal results showed that the trained system achieved 0.36 in MRR and 0.47 in Top5 accuracy. 1 Introduction The conventional Question Answering (QA) archi- tecture is a cascade of the following building blocks: Question Analyzer analyzes a question sentence and identifies the question types (or answer types). Document Retriever retrieves documents related to the question from a large-scale document set. Answer Candidate Extractor extracts answer candidates that match the question types from the retrieved documents. Answer Selector ranks the answer candidates ac- cording to the syntactic and semantic confor- mity of each answer with the question and its context in the document. Typically, question types consist of named en- tities, e.g., PERSON, DATE, and ORGANIZATION, numerical expressions, e.g., LENGTH, WEIGHT, SPEED, and class names, e.g., FLOWER, BIRD, and FOOD. The question type is also used for selecting answer candidates. For example, if the question type of a given question is PERSON, the answer candidate extractor lists only person names that are tagged as the named entity PERSON. The conventional QA architecture has a drawback in that the question-type system restricts the range of questions that can be answered by the system. It is thus problematic for QA system developers to care- fully design and build an answer candidate extrac- tor that works well in conjunction with the question- type system. This problem is particularly difficult when the task is to develop a multilingual QA sys- tem to handle languages that are unfamiliar to the developer. Developing high-quality tools that can extract named entities, numerical expressions, and class names for each foreign language is very costly and time-consuming. Recently, some pioneering studies have inves- tigated approaches to automatically construct QA components from scratch by applying machine learning techniques to training data (Ittycheriah et al., 2001a)(Ittycheriah et al., 2001b)(Ng et al., 2001) (Pasca and Harabagiu)(Suzuki et al., 2002)(Suzuki 215 Table 1: Number of Questions in Question Types of CRL QA Data # of …

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