Adaptive Information Extraction from Text by Rule Induction and Generalisation
(2001) Adaptive Information Extraction from Text by Rule Induction and Generalisation. In Proceedings 17th International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle.
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(LP)2 is a covering algorithm for adaptive Information Extraction from text (IE). It induces symbolic rules that insert SGML tags into texts by learning from examples found in a userdefined tagged corpus. Training is performed in two steps: initially a set of tagging rules is learned; then additional rules are induced to correct mistakes and imprecision in tagging. Induction is performed by bottom-up generalization of examples in the training corpus. Shallow knowledge about Natural Language Processing (NLP) is used in the generalization process. The algorithm has a considerable success story. From a scientific point of view, experiments report excellent results with respect to the current state of the art on two publicly available corpora. From an application point of view, a successful industrial IE tool has been based on (LP)2. Real world applications have been developed and licenses have been released to external companies for building other applications. This paper presents (LP)2, experimental results and applications, and discusses the role of shallow NLP in rule induction.
| Keywords: | Natural language processin, adaptive information extraction, rule induction |
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| Subjects: | AKT Challenges > Knowledge acquisition |
| ID Code: | 118 |
| Deposited By: | Brewster, Christopher |
| Deposited On: | 27 February 2003 |
| Alternative Locations: | http://www.dcs.shef.ac.uk/~fabio/cira-papers.html |
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