Benchmarking T-ANNE: text annotation system

Benjamin Chu, Fadzly Zahari, Dickson Lukose

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

1 Citation (Scopus)


Recognizing, identifying and extracting entities, like Person, Location and Organization are useful for information mining from unstructured texts. Currently, it is a typical way of establishing the content for future use such as filtering, indexing or search. This paper presents the results obtained for the benchmark test on our Text Annotation Engine (T-ANNE) that we have developed against several similar systems. Precision, Recall and F-Measure will be used to measure the results for this evaluation.

Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies, i-KNOW 2012
Publication statusPublished - 2012
Externally publishedYes
EventInternational Conference on Knowledge Management and Knowledge Technologies 2012 - Graz, Austria
Duration: 5 Sept 20127 Sept 2012
Conference number: 12th


ConferenceInternational Conference on Knowledge Management and Knowledge Technologies 2012
Abbreviated titlei-KNOW 2012
Internet address


  • Named entity recognition
  • Natural language processing
  • Text Annotation Engine

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