Benchmarking T-ANNE: text annotation system

Authors:
Benjamin Chu;Fadzly Zahari;Dickson Lukose
Affiliations:
MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur, Malaysia;MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur, Malaysia;MIMOS Berhad, Technology Park Malaysia, Kuala Lumpur, Malaysia
Venue:
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Year:
2012

Citing 3
Cited 0

Efficient string matching: an aid to bibliographic search

Communications of the ACM
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition

CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Usage patterns of collaborative tagging systems

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.