1995 CRYSTAL
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- (Soderland et al., 1995) ⇒ Stephen Soderland, David Fisher, Jonathan Aseltine, Wendy G. Lehnert. (1995). “CRYSTAL: Inducing a Conceptual Dictionary.” In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995).
Subject Headings: CRYSTAL System, Information Extraction System.
Notes
Cited By
~301 http://scholar.google.com/scholar?cites=123177023428241753
2000
- (Kushmerick, 2000) ⇒ Nicholas Kushmerick. (2000). “Wrapper induction: Efficiency and expressiveness.” In: Artificial Intelligence, 118(1-2). doi:10.1016/S0004-3702(99)00100-9.
1999
- (Soderland, 1999) ⇒ Stephen Soderland. (1999). “Learning Information Extraction Rules for Semi-Structured and Free Text.” In: Machine Learning, 44(1-3). doi:10.1023/A:1007562322031
Quotes
Abstract
- One of the central knowledge sources of an information extraction (IE) system is a dictionary of linguistic patterns that can be used to identify references to relevant information in a text. Automatic creation of conceptual dictionaries is important for portability and scalability of an IE system. This paper describes CRYSTAL, a system which automatically induces a dictionary of “concept-node definitions” sufficient to identify relevant information from a training corpus. Each of these concept-node definitions is generalized as far as possible without producing errors, so that a minimum number of dictionary entries cover the positive training instances. Because it tests the accuracy of each proposed definition, CRYSTAL can often surpass human intuitions in creating reliable extraction rules.
3.1 Creating Initial CN Definitions
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- When BADGER analyzes this sentence, it assigns the complex noun phrase “the exception of mild shortness of breath and chronically swollen ankles” to a single prepositional phrase buffer. When a complex noun phrase has multiple head nouns or multiple modifiers, the class constraint becomes a conjunctive constraint.
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