Special Issue on Selected Papers for IEEE ICTAI 2003No Access

GLOBAL RULE INDUCTION FOR INFORMATION EXTRACTION

JING XIAO

School of Computing, National University of Singapore, Singapore 117543, Singapore

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TAT-SENG CHUA

School of Computing, National University of Singapore, Singapore 117543, Singapore

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, and

JIMIN LIU

School of Computing, National University of Singapore, Singapore 117543, Singapore

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https://doi.org/10.1142/S0218213004001831Cited by:2 (Source: Crossref)

Abstract

The ability to extract desired pieces of information from natural language texts is an important task with a growing number of potential applications. This paper presents a novel pattern rule induction learning system, GRID, which emphasizes the use of global feature distribution in all of the training instances in order to make better decision on rule induction. GRID uses chunks as contextual units instead of tokens, and incorporates features at lexical, syntactical and semantic levels simultaneously. The features chosen in GRID are general and they were applied successfully to both semi-structured text and free text. Our experimental results on some publicly available webpage corpora and MUC-4 test set indicate that our approach is effective.

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References

G. Salton and M. J. McGill , Introduction to Modern Information Retrieval ( McGraw-Hill , 1983 ) . Google Scholar
M. E. Califf, Learning Techniques for National Lanuage Information Extraction, PhD thesis, University of Texas at Austin (1998) . Google Scholar
M. E. Califf and R. J. Mooney, Relational Learning of Pattern-Match Rules for Information Extraction. Proc. of the 16th National Conference on Artificial Intelligence. (AAAI-99) (1999) . Google Scholar
S. Soderland, Machine Learning 34, (1999). Google Scholar
S. Muggleton, New Generation Computing, Special Issue on Inductive Logic Programming 13, (1995). Google Scholar
J. M. Zelle, R. J. Mooney, and J. B. Konvisser. Combining Top-down and Bottom-up Methods in Inductive Logic Programming. Proc. of the 11th International Conference on Machine Learning (1994) . Google Scholar
S. Soderland, Learning Text Analysis Rules for Domain-Specific Natural Language Processing. PhD thesis, University of Massachusetts, Amherst. (1997) . Google Scholar
I. Muslea. Extracting Patterns for Information Extraction Tasks: A Survey. The AAAI-99 workshop on Machine Learning for Information Extraction. (1999) . Google Scholar
E. M. Riloff. Automatically Constructing a Dictionary for Information Extraction Tasks. Proc. of the 11th National Conference on Artificial Intelligence. (1993) . Google Scholar
E. M. Riloff. Automatically Generating Extraction Patterns from Untagged Text. Proc. of the 13th National Conference on Artificial Intelligence. (1996) . Google Scholar
S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert. Crystal: Inducing a Conceptual Dictionary. Proc. of the 14th International Joint Conference on Artificial Intelligence (IJCAI-1995). (1995) . Google Scholar
F. Ciravegna, Adaptive Information Extraction from Text by Rule Induction and Generalization. Proc. of the 17th International Joint Conference on Artificial Intelligence. (IJCAI-2001) (2001) . Google Scholar
A. Mikheev, C. Grover, and M. Moens. Description of the LTG System Used for MUC-7. Proc. of the Seventh Message Understanding Conference. (1998) . Google Scholar
H. L. Chieu and H. T. Ng, Named Entity Recognition: A Maximum Entropy Approach Using Global Information. Proc. of the 19th International Conference on Computational Linguistics (COLING-02) (2002) . Google Scholar
A. Douthat, The Message Understanding Conference Scoring Software User's Manual, Proc. of the 7th Message Understanding Conference. (1998) . Google Scholar
G. A. Miller, et al, Introduction to WordNet: An On-line Lexical Database. Available by ftp to priceton.edu . Google Scholar
T. S. Chua and J. Liu, Learning Pattern Rules for Chinese Named Entity Extraction. Proc. of the 18th National Conference on Artificial Intelligence. (AAAI-02) (2002) . Google Scholar
Proc. of the Fourth Message Understanding Conference. (MUC-4). San Mateo, CA: Morgan Kaufmann.(1992) . Google Scholar
ftp://ftp.cs.brown.edu/pub/nlparser/ . Google Scholar
H. L. Chieu, H. T. Ng and Y. K. Lee, Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods Proc. of the 41st Annual Meeting of the Association for Computational Linguistics (ACL-03). (2003) . Google Scholar
http://www.isi.edu/info-agents/RISE/repository.html . Google Scholar
H. L. Chieu and H. T. Ng, A Maximum Entropy Approach to Information Extraction from Semi-structured and Free Text. Proc. of the 18th National Conference on Artificial Intelligence. (AAAI-02) (2002) . Google Scholar
D. Freitag and N. Kushmerick, Boosted Wrapper Induction. ECAI 2000 Workshop on Machine Learning for Information Extraction. (2000) . Google Scholar