Augmenting a semantic verb lexicon with a large scale collection of example sentences

Kentaro Inui, Toru Hirano, Ryu Iida, Atsushi Fujita, Yuji Matsumoto

Research output: Contribution to conferencePaperpeer-review

Abstract

One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.

Original languageEnglish
Pages365-368
Number of pages4
Publication statusPublished - 2006 Jan 1
Externally publishedYes
Event5th International Conference on Language Resources and Evaluation, LREC 2006 - Genoa, Italy
Duration: 2006 May 222006 May 28

Other

Other5th International Conference on Language Resources and Evaluation, LREC 2006
CountryItaly
CityGenoa
Period06/5/2206/5/28

ASJC Scopus subject areas

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

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