Research of POS Tagging Rules Mining Algorithm

Article Preview

Abstract:

Part of speech contains important grammatical information, so it has great significance for the natural language understanding while the words in the sentence are marked on the parts of speech. POS tagging rules based on statistical methods and rule-based method can mining effectively, but its marked accuracy need to be improved. This paper presents a statistical method and rules of the combination of speech tagging rule mining algorithm in order to improve the correct rate of marked.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2836-2840

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Terry Winograd. Procedures as a Representation for Data in a Computer Program for Understanding Natural Language[J]. MIT AI Technical Report 235, 1971, 2.

Google Scholar

[2] Harabagiu. S, M. Pasca, and S. Maiorano. Experiments with Open Domain Textual Question Answering. Proceedings of the 18th COLING Conference. Saarbrucken, Germany. 2000: 292-298.

DOI: 10.3115/990820.990863

Google Scholar

[3] James Allen, Donna Bayon, Myroslava Dzikovska, George Ferguson, Lucian Galescu, and Amanda stent. Towards Conversational Human-Computer Interaction[J]. AI Magazine, (2001).

Google Scholar

[4] Bos, Johan, and Malte Gabsdil. First-order intference and the interpretation of questions and answers. In Proceedings of Gotalog 2000, ed. Massimo Poesio and David Traum, 43-50. Gothenburg Papers in Computational Linguistics 1-5.R. Nicole, Title of paper with only first word capitalized, J. Name Stand. Abbrev., in press.

Google Scholar

[5] U. Hermjakob. Parsing and Question Classification for Question Answering[C]. In Proceedings of the ACL Workshop on Open-Domain Question Answering, Toulouse, France, (2001).

DOI: 10.3115/1117856.1117859

Google Scholar

[6] Androutsopoulos. Natural Language Interfaces to Data bases an Introduction[J]. Natural Language Engineering, 1995: 21-89.

Google Scholar

[7] Weizenbaum. ELIZA-A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communications of the ACM. 1966, 9: 36-45.

DOI: 10.1145/365153.365168

Google Scholar

[8] E. Brill, J. Lin, M. Banko, S. Dumais and A. Ng. Data-Intensive Question Answering[C]. Proceedings of the Tenth Text Retrieval Conference (TREC 2001).

Google Scholar

[9] Ravichandran, D. and E. H. Hovy. Learning Surface Text Patterns for a Question Answering System[C]. In 40th Annual Meeting of the Association for Computational Linguistics (ACL-2002) Conference. Philadelphia, PA, July (2002).

DOI: 10.3115/1073083.1073092

Google Scholar

[10] Paul Cohen, Robert Schrag, Eric Jones, Adam Pease, Albert Lin, Barbara Starr, David Gunning, and Murray Burke. The DARPA high-performance knowledge bases project[C]. AIM agazine, 1998, 12: 25-49.

Google Scholar

[11] Marius Pasca and Sanda Harabagiu. High performance question answering[C]. In Proceedings of the 24th SIGIR Conference on Research and Development in Information Retrieval. 2001: 366-37.

DOI: 10.1145/383952.384025

Google Scholar

[12] F. Rinaldi, J. Dowdall, K. Kaljurand, M. Hess, D. Mo11 G. Exploiting Paraphrases in a Question Answering System[C]. Proc. Workshop in Paraphrasing at ACL2003, Sapporo, Japan. 2003, 7: 25 -32.

DOI: 10.3115/1118984.1118988

Google Scholar