A Discourse Linking Tool for English Language Texts comprising Lexicon building and Ontology Creation
Authors: 
Anuja Bawaskar, Kasturi Adep, Adarsh Jaju, Yashodhara Haribhakta, Krishnanjan Bhattacharjee, Swati Mehta, Ajai KumarAuthors Info & Claims
Anuja Bawaskar, Kasturi Adep, Adarsh Jaju, Yashodhara Haribhakta, Krishnanjan Bhattacharjee, Swati Mehta, Ajai KumarAuthors Info & ClaimsAbstract
Natural Language text is not bound by a fixed structure. For a machine to understand the language, the challenge lies in resolving the ambiguities and capturing innovativeness. Due to its unstructured nature, discourse linking, required for understanding and generating text by a machine is a challenging task. Also, dealing with sentences varied in nature and changing them into generic structure is an additional challenge. This paper presents a way to create a discourse linking tool for English language. This tool is based on specially created lexicon and hand-crafted rules suited for discourse linking purpose. Currently available lexicons such as dictionaries, WordNet, etc. are not suited as they lack domain knowledge. Therefore, an ontology is developed for political news domain which serves as a lexicon and its inherent relations are used for discourse linking purpose.
References
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- A Discourse Linking Tool for English Language Texts comprising Lexicon building and Ontology Creation
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Published In
November 2017
157 pages
ISBN:9781450353243
DOI:10.1145/3154979
Copyright © 2017 ACM.
© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
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Published: 24 November 2017
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ICCCT-2017
ICCCT-2017: International Conference on Computer and Communication Technology
November 24 - 26, 2017
Allahabad, India
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ICCCT-2017 Paper Acceptance Rate 33 of 124 submissions, 27%;
Overall Acceptance Rate 33 of 124 submissions, 27%
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