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CIARP
2007
Springer

Information Extraction and Classification from Free Text Using a Neural Approach

12 years 3 months ago
Information Extraction and Classification from Free Text Using a Neural Approach
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.
Ignazio Gallo, Elisabetta Binaghi
Added 13 Aug 2010
Updated 13 Aug 2010
Type Conference
Year 2007
Where CIARP
Authors Ignazio Gallo, Elisabetta Binaghi
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