Extraction, selection and ranking of Field Association (FA) Terms from domain-specific corpora for building a comprehensive FA terms dictionary

  • Authors:

  • Tshering Cigay Dorji;El-sayed Atlam;Susumu Yata;Masao Fuketa;Kazuhiro Morita;Jun-ichi Aoe

  • Affiliations:

  • University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan;University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan;University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan;University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan;University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan;University of Tokushima, Department of Information Science and Intelligent Systems, Faculty of Engineering, Minamijosanjima 2-1, 770-8506, Tokushima, Japan

  • Venue:
  • Knowledge and Information Systems
  • Year:
  • 2011

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Abstract

Field Association (FA) Terms—words or phrases that serve to identify document fields are effective in document classification, similar file retrieval and passage retrieval. But the problem lies in the lack of an effective method to extract and select relevant FA Terms to build a comprehensive dictionary of FA Terms. This paper presents a new method to extract, select and rank FA Terms from domain-specific corpora using part-of-speech (POS) pattern rules, corpora comparison and modified tf-idf weighting. Experimental evaluation on 21 fields using 306 MB of domain-specific corpora obtained from English Wikipedia dumps selected up to 2,517 FA Terms (single and compound) per field at precision and recall of 74–97 and 65–98. This is better than the traditional methods. The FA Terms dictionary constructed using this method achieved an average accuracy of 97.6% in identifying the fields of 10,077 test documents collected from Wikipedia, Reuters RCV1 corpus and 20 Newsgroup data set.