Using decision trees to infer semantic functions of attribute grammars

S. Zvada, T. Gyimóthy

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper we present a learning method called LAG (Learning of Attribute Grammar) which infers semantic functions for simple classes of attribute grammars by means of examples and background knowledge. This method is an improvement on the AGLEARN approach as it generates the training examples on its own via the effective use of background knowledge. The background knowledge is given in the form of attribute grammars. In addition, the LAG method employs the decision tree learner C4.5 during the learning process. Treating the specification of an attribute grammar as a learning task gives rise to the application of attribute grammars to new sorts of problems such as the Part-of-Speech (PoS) tagging of Hungarian sentences. Here we inferred context rules for selecting the correct annotations for ambiguous words with the help of a background attribute grammar. This attribute grammar detects structural correspondences of the sentences. The rules induced this way were found to be more precise than those rules learned without this information.

Original languageEnglish
Pages (from-to)279-304
Number of pages26
JournalActa Cybernetica
Volume15
Issue number2
Publication statusPublished - 2001

Fingerprint

Attribute Grammars
Decision trees
Decision tree
Semantics
Specifications
Grammar
Tagging
Ambiguous
Learning Process
Sort
Annotation
Correspondence
Specification
Background
Learning

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Using decision trees to infer semantic functions of attribute grammars. / Zvada, S.; Gyimóthy, T.

In: Acta Cybernetica, Vol. 15, No. 2, 2001, p. 279-304.

Research output: Contribution to journalArticle

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