Article,
Learning Information Extraction Patterns From Examples
(1996)
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...
