Automatic acquisition of Chinese novel noun compounds

Meng Wang, Chu-ren Huang, Shiwen Yu, Weiwei Sun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Automatic acquisition of novel compounds is notoriously difficult because most novel compounds have relatively low frequency in a corpus. The current study proposes a new method to deal with the novel compound acquisition challenge. We model this task as a two-class classification problem in which a candidate compound is either classified as a compound or a non-compound. A machine learning method using SVM, incorporating two types of linguistically motivated features: semantic features and character features, is applied to identify rare but valid noun compounds. We explore two kinds of training data: one is virtual training data which is obtained by three statistical scores, i.e. co-occurrence frequency, mutual information and dependent ratio, from the frequent compounds; the other is real training data which is randomly selected from the infrequent compounds. We conduct comparative experiments, and the experimental results show that even with limited direct evidence in the corpus for the novel compounds, we can make full use of the typical frequent compounds to help in the discovery of the novel compounds.
Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010
PublisherEuropean Language Resources Association (ELRA)
Pages630-633
Number of pages4
ISBN (Electronic)2951740867, 9782951740860
Publication statusPublished - 1 Jan 2010
Event7th International Conference on Language Resources and Evaluation, LREC 2010 - Mediterranean Conference Centre, Valletta, Malta
Duration: 17 May 201023 May 2010

Conference

Conference7th International Conference on Language Resources and Evaluation, LREC 2010
CountryMalta
CityValletta
Period17/05/1023/05/10

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences
  • Linguistics and Language
  • Language and Linguistics

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