Summary of the paper

Title An Evaluation of Predicate Argument Clustering using Pseudo-Disambiguation
Authors Christian Scheible
Abstract Schulte im Walde et al. (2008) presented a novel approach to semantic verb classication. The predicate argument model (PAC) presented in their paper models selectional preferences by using soft clustering that incorporates the Expectation Maximization (EM) algorithm and the MDL principle. In this paper, I will show how the model handles the task of differentiating between plausible and implau- sible combinations of verbs, subcategorization frames and arguments by applying the pseudo-disambiguation evaluation method. The predicate argument clustering model will be evaluated in comparison with the latent semantic clustering model by Rooth et al. (1999). In particular, the influences of the model parameters, data frequency, and the individual components of the predicate argument model are examined. The results of these experiments show that (i) the selectional preference model overgeneralizes over arguments for the purpose of a pseudo-disambiguation task and that (ii) pseudo-disambiguation should not be used as a universal indicator for the quality of a model.
Topics Language modelling, Statistical and machine learning methods, Evaluation methodologies
Full paper An Evaluation of Predicate Argument Clustering using Pseudo-Disambiguation
Slides -
Bibtex @InProceedings{SCHEIBLE10.206,
  author = {Christian Scheible},
  title = {An Evaluation of Predicate Argument Clustering using Pseudo-Disambiguation},
  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}
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