Welcome to the InfoSci Platform
Identification and Evaluation of Homographic Puns Using Similarity Methods

Identification and Evaluation of Homographic Puns Using Similarity Methods

Divya Agrawal, Ani Thomas
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 9
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781799860761|DOI: 10.4018/IJAEC.2021010102
Cite Article Cite Article

MLA

Agrawal, Divya, and Ani Thomas. "Identification and Evaluation of Homographic Puns Using Similarity Methods." IJAEC vol.12, no.1 2021: pp.18-26. http://doi.org/10.4018/IJAEC.2021010102

APA

Agrawal, D. & Thomas, A. (2021). Identification and Evaluation of Homographic Puns Using Similarity Methods. International Journal of Applied Evolutionary Computation (IJAEC), 12(1), 18-26. http://doi.org/10.4018/IJAEC.2021010102

Chicago

Agrawal, Divya, and Ani Thomas. "Identification and Evaluation of Homographic Puns Using Similarity Methods," International Journal of Applied Evolutionary Computation (IJAEC) 12, no.1: 18-26. http://doi.org/10.4018/IJAEC.2021010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Natural language processing is a subfield of linguistics concerned with the interactions between computers and human language, specifically in how to program computers to process and analyze large amounts of text data (natural language data). WSD, word sense disambiguation in natural language processing, is the task of determining the correct annotation of the pun word in given context. This paper describes about the endeavor in using cosine similarity method for detection of a single homographic pun in given context, its location, and the correct annotation with respect to helping words in the context. This paper includes two approaches: BIT_SYS1 and BIT_SYS2. The first contains the words having synset count one as it cannot be pun but it can serve as helping word to the pun, and in the later words with synset count one is eliminated and the concept of helping word is abandoned. Performance of BIT_SYS2 is better than BIT_SYS1 as F1 score of BIT_SYS2(0.8571, 1.0000, 1.0000) is higher than BIT_SYS1(0.8439, 0.8648, 0.8648) in pun detection task, pun location task, and pun annotation task.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.