Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention

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This paper introduces an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context.

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11 p.

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Becker, Maria; Staniek, Michael; Nastase, Vivi; Palmer, Alexis & Frank, Anette August 2017.

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This paper introduces an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context.

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11 p.

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Abstract: Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.

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  • 6th Joint Conference on Lexical and Computational Semantics, August 3-4, 2017. Vancouver, Canada.

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  • Publication Title: Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
  • Pages: 230-240
  • Peer Reviewed: Yes

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UNT Scholarly Works

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  • August 2017

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  • Aug. 31, 2017, 5:38 p.m.

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Becker, Maria; Staniek, Michael; Nastase, Vivi; Palmer, Alexis & Frank, Anette. Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention, article, August 2017; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc991481/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Information.