Annotating text segments using a web-based categorization approach

Hsin Chen Chiao, Hsiao Tieh Pu, Lee Feng Chien

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

Abstract

Conventional automatic text annotation tools mostly extract named entities from texts and annotate them with information about persons, locations, and dates, etc. Such kind of entity type information, however, is insufficient for machines to understand the context or facts contained in the texts. This paper presents a general text categorization approach to categorize text segments into broader subject categories, such as categorizing a text string into a category of paper title in Mathematics or a category of conference name in Computer Science. Experimental results confirm its wide applicability to various digital library applications.

Original languageEnglish
Title of host publicationDigital Libraries
Subtitle of host publicationImplementing Strategies and Sharing Experiences - 8th International Conference on Asian Digital Libraries, ICADL 2005, Proceedings
Pages323-331
Number of pages9
DOIs
Publication statusPublished - 2005 Dec 1
Event8th International Conference on Asian Digital Libraries, ICADL 2005 - Bangkok, Thailand
Duration: 2005 Dec 122005 Dec 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3815 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Asian Digital Libraries, ICADL 2005
CountryThailand
CityBangkok
Period05/12/1205/12/15

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chiao, H. C., Pu, H. T., & Chien, L. F. (2005). Annotating text segments using a web-based categorization approach. In Digital Libraries: Implementing Strategies and Sharing Experiences - 8th International Conference on Asian Digital Libraries, ICADL 2005, Proceedings (pp. 323-331). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3815 LNCS). https://doi.org/10.1007/11599517_37