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  • 标题:Probabilistic Distributional Semantics with Latent Variable Models
  • 本地全文:下载
  • 作者:Diarmuid Ó Séaghdha ; Anna Korhonen
  • 期刊名称:Computational Linguistics
  • 印刷版ISSN:0891-2017
  • 电子版ISSN:1530-9312
  • 出版年度:2014
  • 卷号:40
  • 期号:3
  • 页码:587-631
  • DOI:10.1162/COLI_a_00194
  • 语种:English
  • 出版社:MIT Press
  • 摘要:We describe a probabilistic framework for acquiring selectional preferences of linguistic predicates and for using the acquired representations to model the effects of context on word meaning. Our framework uses Bayesian latent-variable models inspired by, and extending, the well-known Latent Dirichlet Allocation (LDA) model of topical structure in documents; when applied to predicate–argument data, topic models automatically induce semantic classes of arguments and assign each predicate a distribution over those classes. We consider LDA and a number of extensions to the model and evaluate them on a variety of semantic prediction tasks, demonstrating that our approach attains state-of-the-art performance. More generally, we argue that probabilistic methods provide an effective and flexible methodology for distributional semantics.
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