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Improving the Performance of Bayesian and Support Vector Classifiers in Word Sense Disambiguation using Positional Information

Tapio Pahikkala, Sampo Pyysalo, Jorma Boberg, Aleksandr Mylläri, Tapio Salakoski, Improving the Performance of Bayesian and Support Vector Classifiers in Word Sense Disambiguation using Positional Information. In: Ville Könönen Matti Pöllä Olli Simula Timo Honkela (Ed.), Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05), 90-97, Helsinki University of Technology, 2005.

Abstract:

We explore word position-sensitive models and their realizations in word sense disambiguation tasks when using Naive Bayes and Support Vector Machine classifiers. It is shown that a straightforward incorporation of word positional information fails to improve the performance of either method on average. However, we demonstrate that our special kernel that takes into account word positions statistically significantly improves the classification performance. For Support Vector Machines, we apply this kernel instead of the ordinary Bag-of-Words kernel, and for the Bayes classifier the kernel is used for smoothed density estimation. We discuss the benefits and drawbacks of position-sensitive and kernel-smoothed models as well as analyze and evaluate the effects of these models on a subset of the Senseval-3 data.

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BibTeX entry:

@INPROCEEDINGS{inpPaPyBoMySa05a,
  title = {Improving the Performance of Bayesian and Support Vector Classifiers in Word Sense Disambiguation using Positional Information},
  booktitle = {Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05)},
  author = {Pahikkala, Tapio and Pyysalo, Sampo and Boberg, Jorma and Mylläri, Aleksandr and Salakoski, Tapio},
  editor = {Timo Honkela, Ville Könönen Matti Pöllä Olli Simula},
  publisher = {Helsinki University of Technology},
  pages = {90-97},
  year = {2005},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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