tailieunhanh - Báo cáo khoa học: "Question Answering by Lexical Fabric and External Resources"

One of the major challenges in TRECstyle question-answering (QA) is to overcome the mismatch in the lexical representations in the query space and document space. This is particularly severe in QA as exact answers, rather than documents, are required in response to questions. Most current approaches overcome the mismatch problem by employing either data redundancy strategy through the use of Web or linguistic resources. This paper investigates the integration of lexical relations and Web knowledge to tackle this problem. The results obtained on TREC11 QA corpus indicate that our approach is both feasible and effective. . | QUALIFIER Question Answering by Lexical Fabric and External Resources Hui Yang Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 yangh@ Tat-Seng Chua Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 chuats@ Abstract One of the major challenges in TREC-style question-answering QA is to overcome the mismatch in the lexical representations in the query space and document space. This is particularly severe in QA as exact answers rather than documents are required in response to questions. Most current approaches overcome the mismatch problem by employing either data redundancy strategy through the use of Web or linguistic resources. This paper investigates the integration of lexical relations and Web knowledge to tackle this problem. The results obtained on TREC11 QA corpus indicate that our approach is both feasible and effective. 1 Introduction Open domain Question Answering QA is an information retrieval paradigm that is attracting increasing attention from the information retrieval IR information extraction IE and natural language processing NLP communities AAAI Spring Symposium Series 2002 ACL-EACL 2002 . A QA system retrieves concise answers to open-domain natural language questions where a large text collection termed the QA corpus is used as the source for these answers. Contrary to traditional IR tasks it is not acceptable for a QA system to retrieve a full document or a paragraph in response to a question. Contrary to traditional IE tasks no prespecified domain restrictions are placed on the questions which may be of any type and in any topic. Modem QA systems must therefore combine the strengths of traditional IR and NLP IE to provide an apposite way to answering questions. The QA task in the TREC conference series Voorhees 2002 has motivated much of the recent works focusing on fact-based short-answer questions. Examples of such .

• Báo cáo khoa học
• Question Answering
• Lexical Fabric
• External Resources
• scientific reports
• model language
• process natural language
• Offline Strategies
• Online Question Answering
• Answering Questions Before They Are Asked
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Question Answering as Question Biased Term Extraction
• A New Approach
• Multilingual QA
• Sentence Dependency Tagging
• Online Question Answering Forums
• Zhonghua Qu and Yang Liu
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Metadata Aware Measures
• Answer Summarization in Community
• Optimizing Question Answering Accuracy
• Maximizing Log Likelihood
• Matthias H
• Heie
• A Co occurrence based Approach
• Multi document Summarization
• A Graph based Semi Supervised Learning
• Asli Celikyilmaz Marcus Thint Zhiheng Huang
• Towards Personalized Models
• Predicting Success in Community Question Answering
• Yandong Liu and Eugene Agichtein Emory
• Learning foci
• Topic Maps
• Automatic Annotation Techniques
• Supervised Complex Question Answering
• Yllias Chali
• Utilizing Co Occurrence
• Answers in Question Answering
• Min Wu
• Answer Extraction
• Semantic Clustering
• Extractive Summarization
• Clinical Question Answering
• Lexical Chains Propagating Verb Arguments
• Adrian Novischi
• Methods for Using Textual Entailment
• Open Domain Question Answering
• Sanda Harabagiu and Andrew Hickl
• The QuALiM Question Answering Demo
• Supplementing Answers
• Paragraphs drawn from Wikipedia
• Language independent Probabilistic
• Answer Ranking
• Opinion and Generic Question Answering Systems
• Performance Analysis
• Alexandra Balahur
• Interactive Question Answering
• Real World Environments
• Andrew Hickl
• A Practical Korean Question
• Answering Framework
• Restricted Domain
• Experiments with Interactive Question Answering
• Sanda Harabagiu
• John Lehmann
• and Dan Moldovan
• Automatic Discovery of Intentions in Text
• Application to Question Answering
• Marta Tatu
• Learning Strategies
• Open Domain Natural Language
• Descriptive Question Answering in Encyclopedia
• Hyo Jung Oh
• Chung Hee Lee
• Splitting Complex Temporal Questions
• Question Answering systems
• E
• Saquete
• P
• Mart
• Constraint Satisfaction
• QA by Dossier with Constraints