Finding within-organisation spatial information on the web
Contribuinte(s) |
Maher, Michael Thiebaux, Sylvie |
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Data(s) |
04/12/2015
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Resumo |
Information available on company websites can help people navigate to the offices of groups and individuals within the company. Automatically retrieving this within-organisation spatial information is a challenging AI problem This paper introduces a novel unsupervised pattern-based method to extract within-organisation spatial information by taking advantage of HTML structure patterns, together with a novel Conditional Random Fields (CRF) based method to identify different categories of within-organisation spatial information. The results show that the proposed method can achieve a high performance in terms of F-Score, indicating that this purely syntactic method based on web search and an analysis of HTML structure is well-suited for retrieving within-organisation spatial information. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/89596/1/Hou-etal-AuAI15.pdf Hou, Jun, Schulz, Ruth, Wyeth, Gordon, & Nayak, Richi (2015) Finding within-organisation spatial information on the web. In Maher, Michael & Thiebaux, Sylvie (Eds.) 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), 30 November – 4 December 2015, Canberra, A.C.T. http://purl.org/au-research/grants/ARC/DP140103216 |
Direitos |
Copyright 2015 [Please consult the author] |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #HTML pattern #Search engine #Spatial information extraction |
Tipo |
Conference Paper |