Monitoring geographical entities with temporal awareness in tweets

Koji Matsuda, Mizuki Sango, Naoaki Okazaki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To extract real-time information referring to a specific place from social network service texts such as tweets, it is necessary to analyze the temporal semantics of the reference. To solve this problem, we created a corpus with multiple annotations for more than 10,000 tweets using crowdsourcing. We constructed an automatic analysis model based on multiple neural networks and compared their characteristics. Our dataset and codes are released in our website (http://www.cl.ecei.tohoku.ac.jp/~matsuda/TA_corpus/).

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers
EditorsAlexander Gelbukh
PublisherSpringer-Verlag
Pages379-390
Number of pages12
ISBN (Print)9783319771151
DOIs
Publication statusPublished - 2018 Jan 1
Event18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 - Budapest, Hungary
Duration: 2017 Apr 172017 Apr 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10762 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017
CountryHungary
CityBudapest
Period17/4/1717/4/23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Matsuda, K., Sango, M., Okazaki, N., & Inui, K. (2018). Monitoring geographical entities with temporal awareness in tweets. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers (pp. 379-390). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10762 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-77116-8_28