NDLI: Extract list data from semi-structured document using clustering

Content Provider	IEEE Xplore Digital Library
Author	Hui Xu Juanzi Li Peng Xu
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China (Hui Xu; Juanzi Li; Peng Xu)
Abstract	This paper is concerned with list data extraction from semi-structured documents. By list data extraction, we mean extracting data from lists and grouping it by rows and columns. List, which has structured characteristics, is used to store highly structured and database-like information in many semi-structured documents, such as business annual reports, online airport listings, catalogs, hotel directories, etc. List data extraction is of benefit to text mining applications on semi-structured documents. Several research efforts have been done on structured data extraction from semi-structured documents by utilizing the word layout and arrangement information. However, as far as we know, few studies have been sufficiently investigated on list data extraction making use of the semantic information previously. In this paper, we propose a clustering based method making use of not only the layout and arrangement information but also the semantic information of words for this extraction task. We show experimental results on plain-text annual reports from Shanghai Stock Exchange, in which 73.49% of the lists were extracted correctly.
Starting Page	559
Ending Page	564
File Size	1233309
Page Count	6
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Text mining Databases Clustering algorithms Humans Computer applications Airports Data mining Catalogs Stock markets
Content Type	Text
Resource Type	Article

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