Summary of the paper

Title Automatic Grammar Rule Extraction and Ranking for Definitions
Authors Claudia Borg, Mike Rosner and Gordon J. Pace
Abstract Plain text corpora contain much information which can only be accessed through human annotation and semantic analysis, which is typically very time consuming to perform. Analysis of such texts at a syntactic or grammatical structure level can however extract some of this information in an automated manner, even if identifying effective rules can be extremely difficult. One such type of implicit information present in texts is that of definitional phrases and sentences. In this paper, we investigate the use of evolutionary algorithms to learn classifiers to discriminate between definitional and non-definitional sentences in non-technical texts, and show how effective grammar-based definition discriminators can be automatically learnt with minor human intervention.
Topics Information Extraction, Information Retrieval, Statistical and machine learning methods, Text mining
Full paper Automatic Grammar Rule Extraction and Ranking for Definitions
Slides -
Bibtex @InProceedings{BORG10.609,
  author = {Claudia Borg and Mike Rosner and Gordon J. Pace},
  title = {Automatic Grammar Rule Extraction and Ranking for Definitions},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
Powered by ELDA © 2010 ELDA/ELRA