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Learning Information Extraction Rules for Semi-structured and Free Text (1999)
| Venue: | Machine Learning |
| Citations: | 438 - 10 self |
BibTeX
@INPROCEEDINGS{Soderland99learninginformation,
author = {Stephen Soderland and Claire Cardie and Raymond Mooney},
title = {Learning Information Extraction Rules for Semi-structured and Free Text},
booktitle = {Machine Learning},
year = {1999},
pages = {233--272}
}
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Abstract
. A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text extraction rules automatically. WHISK is designed to handle text styles ranging from highly structured to free text, including text that is neither rigidly formatted nor composed of grammatical sentences. Such semistructured text has largely been beyond the scope of previous systems. When used in conjunction with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories. Keywords: natural language processing, information extraction, rule learning 1. Information extraction As more and more text becomes available on-line, there is a growing need for systems that extract information automatically from text data. An information extraction (IE) sys...
Keyphrases
free text information extraction information extraction rule previous system on-line text information knowledge-engineering bottleneck text style automatic processing natural language processing news story ie application text data separate set text extraction rule text becomes grammatical sentence semantic tagging syntactic analyzer semistructured text
