Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Text processing with the START natural language system
Text, ConText, and HyperText
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
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Learning search engine specific query transformations for question answering
Proceedings of the 10th international conference on World Wide Web
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Domain-Specific Keyphrase Extraction
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
A layered approach to NLP-based information retrieval
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
REXTOR: a system for generating relations from natural language
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
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Information Retrieval (IR) is a major component in many of our daily activities, with perhaps its most prominent role manifested in search engines. Today's most advanced engines use the keyword-based ("bag of words") paradigm, which concedes some inherent disadvantages. We believe that natural language (NL) is a more user-oriented, context-preservative and intuitive mechanism for web search.In this paper, we explore shallow NLP techniques to support a range of NL queries over an existing keyword-based engine. We present JASE, a web application enveloping the Google search engine, which performs web searches by decomposing input NL queries and generating new queries that are more suitable for the search engine. By using some of Google's syntactic operators and filters, it creates "clever" queries to improve precision.A preliminary evaluation was conducted to test JASE's accuracy, and results have been encouraging. We conclude that the NL model has potential to not only rival the keyword-based paradigm, but substantially surpass it.