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Heuristic and Rule-Based Knowledge Acquisition: Classification of Numeral Strings in Text

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Advances in Knowledge Acquisition and Management (PKAW 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4303))

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Abstract

This paper describes the rule-based classification of numerals and strings that include numerals, composed of a number and semantic unit(s) that indicate a SPEED, NUMBER, or other measure, at three levels: morphological, syntactic, and semantic. The approach employs three interpretation processes: word trigram construction with tokeniser, rule-based processing of number strings, and n-gram based classification. We extracted numeral strings from 378 online newspaper articles, finding that, on average, they comprised about 2.2% of the words in the articles. To manually extract n-gram rules to disambiguate the number strings’ meanings, our approach was trained on 886 numeral strings and tested on the remaining 3251 strings. We implemented two heuristic disambiguation methods based on each category’s frequency statistics collected from the sample data, and precision ratios of both methods were 86.8% and 86.3% respectively. This paper focuses on the acquisition and performance of different types of rules applied to numeral strings classification.

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References

  1. Asahara, M., Matsumoto, Y.: Japanese Named Entity Extraction with Redundant Morpho-logical Analysis. In: Proceedings of HLT-NAACL 2003, pp. 8–15 (2003)

    Google Scholar 

  2. Black, W., Rinaldi, F., Mowatt, D.: FACILE: Description of the NE system used for MUC-7. In: Proceedings of MUC-7 (1998)

    Google Scholar 

  3. Chieu, L., Ng, T.: Named Entity Recognition: A Maximum Entropy Approach Using Global Information. In: Proceedings of the 19th COLING, pp. 190–196 (2002)

    Google Scholar 

  4. CoNLL-2003 Language-Independent Named Entity Recognition (2003), http://www.cnts.uia.ac.be/conll2003/ner/2

  5. Dale, R.: A Framework for Complex Tokenisation and its Application to Newspaper Text. In: Proceedings of the second Australian Document Computing Symposium (1997)

    Google Scholar 

  6. Earley, J.: An Efficient Context-Free Parsing Algorithm. CACM 13(2), 94–102 (1970)

    MATH  Google Scholar 

  7. Maynard, D., Tablan, V., Ursu, C., Cunningham, H., Wilks, Y.: Named Entity Recognition from Diverse Text Types. In: Proceedings of Recent Advances in NLP (2001)

    Google Scholar 

  8. Nelson, G., Wallis, S., Aarts, B.: Exlporing Natural Language - working with the British Component of the International Corpus of English. John Benjamins, The Netherlands (2002)

    Google Scholar 

  9. Polanyi, L., van den Berg, M.: Logical Structure and Discourse Anaphora Resolution. In: Proceedings of ACL99 Workshop on The Relation of Discourse/Dialogue Structure and Reference, pp. 10–117 (1999)

    Google Scholar 

  10. Reiter, E., Sripada, S.: Learning the Meaning and Usage of Time Phrases from a parallel Text-Data Corpus. In: Proceedings of HLT-NAACL2003 Workshop on Learning Word Meaning from Non-Linguistic Data, pp. 78–85 (2003)

    Google Scholar 

  11. Siegel, M., Bender, E.M.: Efficient Deep Processing of Japanese. In: Proceedings of the 3rd Workshop on Asian Language Resources and International Standardization (2002)

    Google Scholar 

  12. Torii, M., Kamboj, S., Vijay-Shanker, K.: An investigation of Various Information Sources for Classifying Biological Names. In: Proceedings of ACL2003 Workshop on Natural Language Processing in Biomedicine, pp. 113–120 (2003)

    Google Scholar 

  13. Wang, H., Yu, S.: The Semantic Knowledge-base of Contemporary Chinese and its Apllication in WSD. In: Proceedings of the Second SIGHAN Workshop on Chinese Language Processing, pp. 112–118 (2003)

    Google Scholar 

  14. Zhou, G., Su, J.: Named Entity Recognition using an HMM-based Chunk Tagger. In: Proceedings of ACL 2002, pp. 473–480 (2002)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Min, K., MacDonell, S., Moon, YJ. (2006). Heuristic and Rule-Based Knowledge Acquisition: Classification of Numeral Strings in Text. In: Hoffmann, A., Kang, Bh., Richards, D., Tsumoto, S. (eds) Advances in Knowledge Acquisition and Management. PKAW 2006. Lecture Notes in Computer Science(), vol 4303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11961239_4

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  • DOI: https://doi.org/10.1007/11961239_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68955-3

  • Online ISBN: 978-3-540-68957-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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