SCOPE

With the increasing popularity and availability of Internet-based technologies, as well as the proliferation of digital computing devices and their use in communication, huge amounts of Human generated content is produced every day in the form of documents, email, instant messaging, social network sites, blogs, and other textual corpora. As a result, we have witnessed an increased demand for systems and algorithms capable of mining textual data, seeking interesting characteristics, hidden patterns, structure, trends, knowledge and key relationships within these large textual corpora. Text mining, combining the disciplines of data mining, information extraction, information retrieval, text categorization, probabilistic modeling, linear algebra, machine learning, and computational linguistics, is a new interdisciplinary field that emerged to address these issues. Examples of emergent applications include metadata generation, visualization techniques, information extraction, text segmentation and classification, text summarization, and trend analysis, to name a few.
This special session aims at sharing new ideas and works on models and approaches for improving over state of the art techniques for mining unstructured, semi-structured, and fully structured textual data.

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