Knowledge Building via Optimally Clustered Word Embedding with Hierarchical Clustering

Nadeesha Pathirana, Sandaru Seneviratne, Rangika Samarawickrama, Shane Wolff, Charith Chitraranjan, Uthayasanker Thayasivam, Tharindu Ranasinghe

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Given a particular domain, drawing out information from a vast amount of data is not an easy task. Data may be adequate and abundant but analysis of data requires a great deal of work. Therefore, manual construction of a knowledge base on any particular domain is more time consuming and it requires much human intervention. The process can be semi-automated by effectively using word embedding to identify the semantics. The words can then be clustered using hierarchical clustering. This study proposes a novel approach on how knowledge discovery can be semiautomated for the restaurant domain by effectively identifying the optimal number of clusters from hierarchical clustering of words extracted from restaurant reviews.
Original languageEnglish
Title of host publication15th International Conference on Natural Language Processing
Pages69-73
Number of pages5
Publication statusPublished - Dec 2018
Event15th International Conference on Natural Language Processing - Punjabi University, Patiala, India
Duration: 15 Dec 201818 Dec 2018
https://blogs.iiit.ac.in/monthly_news/15th-international-conference-on-natural-language-processing/

Conference

Conference15th International Conference on Natural Language Processing
Abbreviated title!CON 2018
Country/TerritoryIndia
CityPatiala
Period15/12/1818/12/18
Internet address

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