Sociolinguistically Informed Natural Language Processing: Automating Irony Detection

Abstract

The major goals of this project are summarized by the following aims: Aim 1. To collect and annotate a high-quality corpus to facilitate research on irony detection. Prior to this project, no such high-quality dataset existed. This has been a major obstacle to progress on automated irony detection. Aim 2. To analyze when existing ML and NLP technologies fail to detect ironic intent empirically. We specifically proposed to assess quantitatively (using the collected dataset) whether context is necessary to discern ironic intent(and how often this is the case).Aim 3. Develop a new approach to irony detection that instantiates sociolinguistic conceptions of irony within a modern, probabilistic machine learning framework. This approach is to be informed by theoretical sociolinguistic perspectives on irony (and thus likely capable of discerning ironic utterances missed by existing computational models), while also being practical enough to be operational.

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Document Details

Document Type
Technical Report
Publication Date
Oct 23, 2017
Accession Number
AD1051278

Entities

People

  • Byron C Wallace
  • David Beaver

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Cognitive Science
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Science
  • Computers
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Natural Language Processing
  • Network Science
  • Neural Networks
  • Online Communications
  • Social Media
  • Supervised Machine Learning

Readers

  • Computational Linguistics
  • Educational Psychology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks