Fecha: 2-4 febrero de 2011

Ponente: Roser Morante (CLiPS - University of Antwerp)

Lugar de celebración: Sala Adoración de Miguel (1.2.C16), Escuela Politécnica Superior, UC3M

Resumen: Text data mining has been described as techniques for examining document collections and discovering information not contained in any individual document. This seminar provides an introduction to the main text mining techniques and tasks. In addition, the seminar outlines concepts concerning negation and modality and their crucial role in a wide range of language applications. The treatment of modality and negation is very relevant for all Natural Language Processing (NLP) applications that involve deep text understanding. This includes applications that need to discriminate between factual and non-factual information (uncertain facts, opinions, attitudes, emotions, and beliefs), such as information extraction, opinion mining, sentiment analysis, text mining, and question answering, as well as other applications that process the meaning of texts, such as recognizing textual entailment, paraphrasing, and summarization. Hence, the adequate modeling of these phenomena is of crucial importance to the NLP community as a whole. While the area is still relatively new compared to areas like machine translation, parsing or semantic role labeling, it is now growing quickly.