Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/1673
Title: | Information extraction from structured documents using k-testable tree automaton inference | Authors: | Kosala, R. Blockeel, H. BRUYNOOGHE, Rosemie VAN DEN BUSSCHE, Jan |
Issue Date: | 2006 | Publisher: | Elsevier | Source: | DATA & KNOWLEDGE ENGINEERING, 58(2). p. 129-158 | Abstract: | Information extraction (IE) addresses the problem of extracting specific information from a collection of documents. Much of the previous work on IE from structured documents, such as HTML or XML, uses learning techniques that are based on strings, such as finite automata induction. These methods do not exploit the tree structure of the documents. A natural way to do this is to induce tree automata, which are like finite state automata but parse trees instead of strings. In this work, we explore induction of k-testable ranked tree automata from a small set of annotated examples. We describe three variants which differ in the way they generalize the inferred automaton. Experimental results on a set of benchmark data sets show that our approach compares favorably to string-based approaches. However, the quality of the extraction is still suboptimal. | Keywords: | information extraction; wrapper induction; tree automata; machine; learning; SEMISTRUCTURED DATA; WRAPPER INDUCTION; LANGUAGES | Document URI: | http://hdl.handle.net/1942/1673 | ISSN: | 0169-023X | e-ISSN: | 1872-6933 | DOI: | 10.1016/j.datak.2005.05.002 | ISI #: | 000238602500002 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2007 |
Appears in Collections: | Research publications |
Show full item record
SCOPUSTM
Citations
26
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
15
checked on Sep 26, 2024
Page view(s)
84
checked on Jun 14, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.