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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Gallo, I., & Binaghi, E. (2007). Information extraction and classification from free text using a neural approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4756 LNCS, pp. 921–929). https://doi.org/10.1007/978-3-540-76725-1_95
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