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

Thumbnail image of item number 1 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 2 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 3 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 4 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Showing 1-4 of 11 pages in this article.

PDF Version Also Available for Download.

Description

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

Physical Description

11 p.

Creation Information

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

Context

This article is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Information to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 271 times. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Authors

Publisher

Provided By

UNT College of Information

Situated at the intersection of people, technology, and information, the College of Information's faculty, staff and students invest in innovative research, collaborative partnerships, and student-centered education to serve a global information society. The college offers programs of study in information science, learning technologies, and linguistics.

About | Browse this Partner

Contact Us

Corrections Questions

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

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

Physical Description

11 p.

Notes

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.

Source

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

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

Publication Information

  • Publication Title: Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
  • Pages: 11
  • Page Start: 230
  • Page End: 240
  • Peer Reviewed: Yes

Collections

This article is part of the following collection of related materials.

UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

About | Browse this Collection

What responsibilities do I have when using this article?

Digital Files

When

Dates and time periods associated with this article.

Creation Date

  • August 2017

Added to The UNT Digital Library

  • Aug. 31, 2017, 5:38 p.m.

Description Last Updated

  • Nov. 14, 2023, 10:14 a.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 271
More Statistics

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

Thumbnail image of item number 1 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 2 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 3 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.
Thumbnail image of item number 4 in: 'Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention'.

PDF Version Also Available for Download.

Citations, Rights, Re-Use

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Links for Robots

Helpful links in machine-readable formats.

Archival Resource Key (ARK)

International Image Interoperability Framework (IIIF)

Metadata Formats

Images

URLs

Stats

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. (https://digital.library.unt.edu/ark:/67531/metadc991481/: accessed June 7, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Information.

Back to Top of Screen