Learning information extraction rules for semi-structured and free text

被引：947

作者：

Soderland, S ^{[1
]}

机构：

[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA

来源：

MACHINE LEARNING | 1999年 / 34卷 / 1-3期

关键词：

natural language processing; information extraction; rule learning;

D O I：

10.1023/A:1007562322031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

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 semi-structured 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.

引用

页码：233 / 272

页数：40

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