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A tree-based learning approach for document structure analysis and its application to web search

Published online by Cambridge University Press:  24 February 2014

F. CANAN PEMBE  and
TUNGA GÜNGÖR
F. CANAN PEMBE
Affiliation:
TÜBİTAK BİLGEM, 41470, Gebze, Kocaeli, Turkey e-mail: [email protected]
TUNGA GÜNGÖR
Affiliation:
Department of Computer Engineering, Boğaziçi University, İstanbul, 34342, Turkey e-mail: [email protected]
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Abstract

In this paper, we study the problem of structural analysis of Web documents aiming at extracting the sectional hierarchy of a document. In general, a document can be represented as a hierarchy of sections and subsections with corresponding headings and subheadings. We developed two machine learning models: heading extraction model and hierarchy extraction model. Heading extraction was formulated as a classification problem whereas a tree-based learning approach was employed in hierarchy extraction. For this purpose, we developed an incremental learning algorithm based on support vector machines and perceptrons. The models were evaluated in detail with respect to the performance of the heading and hierarchy extraction tasks. For comparison, a baseline rule-based approach was used that relies on heuristics and HTML document object model tree processing. The machine learning approach, which is a fully automatic approach, outperformed the rule-based approach. We also analyzed the effect of document structuring on automatic summarization in the context of Web search. The results of the task-based evaluation on TREC queries showed that structured summaries are superior to unstructured summaries both in terms of accuracy and user ratings, and enable the users to determine the relevancy of search results more accurately than search engine snippets.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Issue 4 , August 2015 , pp. 569 - 605
Copyright
Copyright © Cambridge University Press 2014 

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