MURAX: a robust linguistic approach for question answering using an on-line encyclopedia
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
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Auto-FAQ: an experiment in cyberspace leveraging
Computer Networks and ISDN Systems
The anatomy of a large-scale hypertextual Web search engine
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Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Focused crawling: a new approach to topic-specific Web resource discovery
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Building a question answering test collection
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Question Answering from Frequently Asked Question Files: Experiences with the FAQ Finder System
Building a large annotated corpus of English: the penn treebank
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Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A new statistical parser based on bigram lexical dependencies
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KGCL: A Knowledge-Grid-Based Cooperative Learning Environment
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Meta-knowledge Annotation for Efficient Natural-Language Question-Answering
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Beyond keywords: Automated question answering on the web
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
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EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Searching for common sense: populating Cyc™ from the web
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised named-entity extraction from the Web: An experimental study
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MLQA '06 Proceedings of the Workshop on Multilingual Question Answering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Knowledge and reasoning for question answering: Research perspectives
Information Processing and Management: an International Journal
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TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
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AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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An approach to answer selection in question-answering based on semantic relations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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The wealth of information on the web makes it an attractive resource for seeking quick answers to simple, factual questions such as "e;who was the first American in space?"e; or "e;what is the second tallest mountain in the world?"e; Yet today's most advanced web search services (e.g., Google and AskJeeves) make it surprisingly tedious to locate answers to such questions. In this paper, we extend question-answering techniques, first studied in the information retrieval literature, to the web and experimentally evaluate their performance.First we introduce Mulder, which we believe to be the first general-purpose, fully-automated question-answering system available on the web. Second, we describe Mulder's architecture, which relies on multiple search-engine queries, natural-language parsing, and a novel voting procedure to yield reliable answers coupled with high recall. Finally, we compare Mulder's performance to that of Google and AskJeeves on questions drawn from the TREC-8 question answering track. We find that Mulder's recall is more than a factor of three higher than that of AskJeeves. In addition, we find that Google requires 6.6 times as much user effort to achieve the same level of recall as Mulder.