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Semi-supervised Classification of Twitter Messages for Organization Name Disambiguation

  • Fujitsu
  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we probe the problem of organization name disambiguation on twitter messages. This task is challenging due to the fact of lacking sufficient information in a tweet message. Instead of conventional methods based on mining external information from web sources to enrich information about organization, we propose to mine the relationship among tweets in data set to utilize context information for disambiguation. With a small scale of labeled tweets, we propose LP-based and TSVM-based semi-supervised methods to classify tweets. We aim to mine both related and non-related information for a given organization. The experiments on WePS-3 show that proposed methods are effective.

Original languageEnglish
Title of host publication6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference
EditorsRuslan Mitkov, Jong C. Park
PublisherAsian Federation of Natural Language Processing
Pages869-873
Number of pages5
ISBN (Electronic)9784990734800
StatePublished - 2013
Externally publishedYes
Event6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Nagoya, Japan
Duration: 14 Oct 2013 → …

Publication series

Name6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Conference

Conference6th International Joint Conference on Natural Language Processing, IJCNLP 2013
Country/TerritoryJapan
CityNagoya
Period14/10/13 → …

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