Effectiveness of Methods for Syntactic and Semantic Recognition of Numeral Strings: Tradeoffs Between Number of Features and Length of Word N-Grams

  • Kyongho Min
  • William H. Wilson
  • Byeong-Ho Kang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)


This paper describes and compares the use of methods based on N-grams (specifically trigrams and pentagrams), together with five features, to recognise the syntactic and semantic categories of numeral strings representing money, number, date, etc., in texts. The system employs three interpretation processes: word N-grams construction with a tokeniser; rule-based processing of numeral strings; and N-gram-based classification. We extracted numeral strings from 1,111 online newspaper articles. For numeral strings interpretation, we chose 112 (10%) of 1,111 articles to provide unseen test data (1,278 numeral strings), and used the remaining 999 articles to provide 11,525 numeral strings for use in extracting N-gram-based constraints to disambiguate meanings of the numeral strings. The word trigrams method resulted in 83.8% precision, 81.2% recall ratio, and 82.5% in F-measurement ratio. The word pentagrams method resulted in 86.6% precision, 82.9% recall ratio, and 84.7% in F-measurement ratio.


numeral strings N-grams named entity recognition natural language processing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kyongho Min
    • 1
  • William H. Wilson
    • 1
  • Byeong-Ho Kang
    • 2
  1. 1.School of Computer Science and Engineering, University of New South Wales, SydneyAustralia
  2. 2.School of Computing, University of Tasmania, HobartAustralia

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