Dynamic-keyword extraction from social media

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

1 Citation (Scopus)

Abstract

Traditional keyword extraction methods make the assumption that corpora is static. However, in social media, information is highly dynamic, with individual words showing a dynamic behaviour. In this paper we propose an unsupervised approach that jointly models words’ temporal behaviour and keyword’s semantic affinity, to address the task of dynamic-keyword extraction. Experiments show the method effectiveness and confirm the importance of exploiting keyword dynamics.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings
EditorsNorbert Fuhr, Leif Azzopardi, Benno Stein, Claudia Hauff, Philipp Mayr, Djoerd Hiemstra
Place of PublicationCham
PublisherSpringer
Pages852-860
Number of pages9
ISBN (Electronic)978-3-030-15712-8
ISBN (Print)978-3-030-15711-1
DOIs
Publication statusPublished - 1 Jan 2019
Event41st European Conference on Information Retrieval, ECIR 2019 - Cologne, Germany
Duration: 14 Apr 201918 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11437 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference41st European Conference on Information Retrieval, ECIR 2019
CountryGermany
CityCologne
Period14/04/1918/04/19

Keywords

  • Dynamic keyword extraction
  • Information extraction
  • Social media

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  • Cite this

    Semedo, D., & Magalhães, J. (2019). Dynamic-keyword extraction from social media. In N. Fuhr, L. Azzopardi, B. Stein, C. Hauff, P. Mayr, & D. Hiemstra (Eds.), Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings (pp. 852-860). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11437 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-030-15712-8_62