Concept Extraction Challenge: University of Twente at MSM2013

Mena B. Habib, Maurice Van Keulen, Zhemin Zhu

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

8 Citations (Scopus)

Abstract

Twitter messages are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Classification (NEC) for tweets. The system uses the power of the Conditional Random Fields (CRF) and the Support Vector Machines (SVM) in a hybrid way to achieve better results. For named entity type classification we use AIDA disambiguation system to disambiguate the extracted named entities and hence find their type.
Original languageEnglish
Title of host publicationMaking Sense of Microposts (MSM2013) Concept Extraction Challenge
Pages17-20
Number of pages4
Publication statusPublished - 2013

Keywords

  • Named Entity Extraction
  • Named Entity Classification
  • Social Media Analysis
  • Twitter Messages

Cite this

Habib, M. B., Keulen, M. V., & Zhu, Z. (2013). Concept Extraction Challenge: University of Twente at MSM2013. In Making Sense of Microposts (MSM2013) Concept Extraction Challenge (pp. 17-20)