Accession Number : AD1051278


Title :   Sociolinguistically Informed Natural Language Processing: Automating Irony Detection


Descriptive Note : Technical Report,18 Jul 2014,17 Jul 2017


Corporate Author : University of Texas at Austin Austin United States


Personal Author(s) : Wallace, Byron C ; Beaver,David


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1051278.pdf


Report Date : 23 Oct 2017


Pagination or Media Count : 35


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.


Descriptors :   data set , artificial intelligence , natural language computing , machine learning , computational linguistics


Subject Categories : Cybernetics
      Information Science


Distribution Statement : APPROVED FOR PUBLIC RELEASE