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Interactive Natural Language Processing

KeyboardThe current state of the art in different areas of natural language processing (NLP) is very far from allowing fully automatic high quality results (HQRs), therefore human intervention is required to correct the output of the NLP engines. This applies specifically to NLP fields such as: machine translation and cross-language processing, text recognition, parsing, speech recognition, information retrieval, etc.

Its goal is to produce HQRs through a tight collaboration between a human operator and a NLP system, following an interactive-predictive paradigm. On the other hand, interactivity offers a unique context in which the feedback provided by the human can be used as new training data for adapting the NLP systems to new environments.

Automatic Speech Recognition and Understanding

Dion speechHuge amounts of audiovisual media are generated on a daily basis: parliamentary session, private meetings, TV and radio shows, public speeches, medical recordings, and many more. The magnitude of such quantity of information makes it impossible to be managed efficiently solely by human intervention. Automatic Speech Recognition and Understanding (ASRU) comes in handy when managing and indexing automatically such large amounts of audiovisual content.

Time Series Analysis

Timeseries appear in a variety of disciples, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. Modern timeseries applications include financial timeseries prediction, video-tracking, music analysis, control theory and genetic sequence analysis.