Text classification has been widely used to assist users with the discovery of useful
information from the Internet. However, traditional classification methods are based on the …

Document classification presents difficult challenges due to the sparsity and the high
dimensionality of text data, and to the complex semantics of the natural language. The …

When humans approach the task of text categorization, they interpret the specific wording of
the document in the much larger context of their background knowledge and experience. On …

Multi-category classification of short dialogues is a common task performed by humans.
When assigning a question to an expert, a customer service operator tries to classify the …

Most existing methods for text categorization employ induction algorithms that use the words
appearing in the training documents as features. While they perform well in many …

A traditional classification approach based on keyword matching represents each text
document as a set of keywords, without considering the semantic information, thereby …

We propose a novel approach to the classification of short texts based on two factors: the
use of Wikipedia-based annotators that have been recently introduced to detect the main …

We enhance machine learning algorithms for text categorization with generated features
based on domain-specific and common-sense knowledge. This knowledge is represented …

Current text classification systems typically use term stems for representing document
content. Semantic Web technologies allow the usage of features on a higher semantic level …

The access to Web distributed information often requires the detection and filtering of the
target objects (eg texts) before specialized automatic retrieval processes can be applied. In …