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Information Extraction for Call for Paper

Information Extraction for Call for Paper

Laurent Issertial, Hiroshi Tsuji
Copyright: © 2020 |Pages: 16
ISBN13: 9781799809517|ISBN10: 179980951X|EISBN13: 9781799809524
DOI: 10.4018/978-1-7998-0951-7.ch020
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MLA

Issertial, Laurent, and Hiroshi Tsuji. "Information Extraction for Call for Paper." Natural Language Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 394-409. https://doi.org/10.4018/978-1-7998-0951-7.ch020

APA

Issertial, L. & Tsuji, H. (2020). Information Extraction for Call for Paper. In I. Management Association (Ed.), Natural Language Processing: Concepts, Methodologies, Tools, and Applications (pp. 394-409). IGI Global. https://doi.org/10.4018/978-1-7998-0951-7.ch020

Chicago

Issertial, Laurent, and Hiroshi Tsuji. "Information Extraction for Call for Paper." In Natural Language Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 394-409. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0951-7.ch020

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Abstract

This paper proposes a system called CFP Manager specialized on IT field and designed to ease the process of searching conference suitable to one's need. At present, the handling of CFP faces two problems: for emails, the huge quantity of CFP received can be easily skimmed through. For websites, the reviewing of some of the main CFP aggregators available online points out the lack of usable criteria. This system proposes to answer to these problems via its architecture consisting of three components: firstly an Information Extraction module extracting relevant information (as date, location, etc...) from CFP using rule based text mining algorithm. The second component enriches the now extracted data with external one from ontology models. Finally the last one displays the said data and allows the end user to perform complex queries on the CFP dataset and thus allow him to only access to CFP suitable for him. In order to validate the authors' proposal, they eventually process the well-known precision / recall metric on our information extraction component with an average of 0.95 for precision and 0.91 for recall on three different 100 CFP dataset. This paper finally discusses the validity of our approach by confronting our system for different queries with two systems already available online (WikiCFP and IEEE Conference Search) and basic text searching approach standing for searching in an email box. On a 100 CFP dataset with the wide variety of usable data and the possibility to perform complex queries we surpass basic text searching method and WikiCFP by not returning the false positive usually returned by them and find a result close to the IEEE system.

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