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Title: Information extraction from structured documents using k-testable tree automaton inference
Authors: Kosala, R.
Blockeel, H.
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:
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

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