PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Information Extraction and Classification from Free Text Using a Neural Approach
Ignazio Gallo and Elisabetta Binaghi
Lecture Notes in Computer Science, 2007(4789 ) pp. 921-929, 2007. ISSN 03029743

Abstract

Many approaches to Information Extraction (IE) have been proposed in literature capable of finding and extract specific facts in relatively unstructured documents. Their application in a large information space makes data ready for post-processing which is crucial to many context such as Web mining and searching tools. This paper proposes a new IE strategy, based on symbolic and neural techniques, and tests it experimentally within the price comparison service domain. In particular the strategy seeks to locate a set of atomic elements in free text which is preliminarily extracted from web documents and subsequently classify them assigning a class label representing a specific product.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:3994
Deposited By:Ignazio Gallo
Deposited On:25 February 2008