Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

Text Categorization with Semantic Commonsense Knowledge

Most of text categorization research exploit bag-of-words text representation. In this approach, however, all contextual information contained in text is neglected. Therefore, capturing semantic similarity between text documents that share very little or even no vocabulary is not possible. In this paper we present an approach that combines well established kernel text classifiers with external ...

Building Semantic/Ontological Knowledge By Text Mining

Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements

The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure...

Text Categorization with Semantic Commonsense Knowledge: First Results

Most of text categorization research exploit bag-of-words text representation. However, such representation makes it very hard to capture semantic similarity between text documents that share very little or even no vocabulary. In this paper we present preliminary results obtained with a novel approach that combines well established kernel text classifiers with external contextual commonsense kn...

Data Mining and the Text Categorization Framework

The aim of this contribution is to show one of the most important application of text mining. According to a wide part of the literature regarding the aforementioned field, great relevance is given to the classification task (Drucker et al., 1999, Nigam et al., 2000). The application contexts are several and multitask, from text filtering (Belkin & Croft, 1992) to word sense disambiguation (Gal...