This course focus on the development of practical applications which involve the use of natural language technology. The course will introduce NLP concepts which will be reinforced by the development, testing, and evaluation of technology in demonstration sessions. Applications to be studied in the course include: Information Extraction, Question Answering, and Text Summarization. None of the applications will be studied in detail, the main objective of the course is to promote the use of NLP and to facilitate access to available technology which can be adapted to specific application domains so that students can go home motivated to develop their own tools/systems. Detailed content: – Overview of Natural Language Processing technologies including parts of speech tagging, named entity recognition, parsing, semantic interpretation and coreference resolution. – Natural Language Technology for Information access: existent systems and projects combining advanced NLP will be presented (e.g. Cubreporter project). – Information Extraction: named entity recognition, relation extraction, event extraction, rule-based and machine learning approaches, evaluation, MUC. – Question Answering: QA architecture, questions and answers, passage selection, answer identification, evaluation, TREC/QA. – Text Summarization: sentence extraction, superficial features for sentence extraction, feature combination, multi-document summarization, evaluation, Document Understanding Conference.