Deterministic Choices in a Data-driven Parser

Sardar Jaf, Allan Ramsay

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Data-driven parsers rely on recommendations from parse models, which are generated from a set of training data using a machine learning classifier, to perform parse operations. However, unless the training data covers every possible situation there may be cases where a parse model is unable to make a recommendation. Therefore, when a parse model recommends no/several parse actions to a parser, it will be hard for a parser to make an informed decision as to what parse operation to perform. Here we examine the effect of various deterministic choices on a data-driven parser when it is presented with no/several recommendation from a parse model.
Original languageEnglish
Title of host publicationThe 12th International Workshop on Natural Language Processing and Cognitive Science
Place of PublicationPoland
Publication statusPublished - 22 Sept 2015
EventThe 12th International Workshop on Natural Language Processing and Cognitive Science - Jagiellonian University
Duration: 22 Sept 201524 Sept 2015

Conference

ConferenceThe 12th International Workshop on Natural Language Processing and Cognitive Science
CityJagiellonian University
Period22/09/1524/09/15

Keywords

  • Data-driven parsing
  • Deterministic parsing
  • Natural language parsing
  • Arabic parsing

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