Learning Information Extraction Patterns From Examples (1995)
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| Venue: | Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing |
| Citations: | 63 - 2 self |
BibTeX
@INPROCEEDINGS{Huffman95learninginformation,
author = {Scott Huffman},
title = {Learning Information Extraction Patterns From Examples},
booktitle = {Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing},
year = {1995},
pages = {246--260},
publisher = {Springer}
}
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Abstract
A growing population of users want to extract a growing variety of information from on-line texts. Unfortunately, current information extraction systems typically require experts to hand-build dictionaries of extraction patterns for each new type of information to be extracted. This paper presents a system that can learn dictionaries of extraction patterns directly from user-provided examples of texts and events to be extracted from them. The system, called LIEP, learns patterns that recognize relationships between key constituents based on local syntax. Sets of patterns learned by LIEP for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns. 1 Introduction Although significant progress has been made on information extraction systems in recent years (for instance through the MUC conferences [MUC, 1992; MUC, 1993]), coding the knowledge these systems need to extract new kinds of information and events is an arduous and time-consuming process [Ril...
