TAILIEUCHUNG - Báo cáo khoa học: "Question Answering Using Web News as Knowledge Base"

AnswerBus News Engine' is a question answering system using the contents of CNN Web site 2 as its knowledge base. Comparing to other question answering systems including its previous versions, it has a totally independent crawling and indexing system and a fully functioning search engine. Because of its dynamic and continuous indexing, it is possible to answer questions on just-happened facts. Again, it reaches high correct answer rate. In this demonstration we will present the living system as well as its new technical features. . | Question Answering Using Web News as Knowledge Base Zhiping Zheng Computational Linguistics Department Saarland University D-66041 Saarbriicken Germany zheng@ Abstract AnswerBus News Engine1 is a question answering system using the contents of CNN Web site2 as its knowledge base. Comparing to other question answering systems including its previous versions it has a totally independent crawling and indexing system and a fully functioning search engine. Because of its dynamic and continuous indexing it is possible to answer questions on just-happened facts. Again it reaches high correct answer rate. In this demonstration we will present the living system as well as its new technical features. Keywords question answering QA specific indexing search engine 1 Introduction AnswerBus3 2 3 is originally designed as a Web-based open-domain question answering QA system. It successfully uses natural language processing and information retrieval techniques and reaches very high correct answer rate. Although it is not designed for TREC it still correctly answers over 70 of TREC-8 questions with Web resources. Because we use commercial search engines as the search tools for the system we don t know if special indexing system and other possible techniques will work better for the QA tasks. In the new experiment we used the contents of CNN Web site and developed a QA system called AnswerBus News Engine to automatically answer news related questions. We chose CNN Web site as the knowledge base because it has a good archive of news stories since 1996 and the CNN Web site seems having good reputation on timely updating. The goal of this experiment is to use most techniques used in AnswerBus QA system together with some new techniques such as QA specific indexing described in 2 3 but not fully implemented in original AnswerBus system and build a QA system to answer time sensitive questions in the real world. Before building the AnswerBus News Engine we did another .

• Báo cáo khoa học
• Question Answering
• Web News
• Knowledge Base
• 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