Discourse parsing and summarization for recommendation systems
Barbara Di Eugenio
Department of Computer Science
University of Illinois at Chicago
Chicago, IL, USA
DEI - Sala conferenze
26 maggio 2011
For over ten years, the NLP Lab at UIC has been engaged in the processing of extended text (discourse) and dialogue (conversations), with applications in discourse parsing, summarization, and educational technology. This talk will provide an overview of two recent projects. The first, on discourse parsing, presents a first-order logic learning approach to determine rhetorical relations between discourse segments. Beyond linguistic cues and lexical information, our approach exploits compositional semantics. We report a statistically significant improvement in classifying relations over attribute-value learning paradigms such as Decision Trees, RIPPER and Naive Bayes.
The second project concerns a summarizer we developed for a Music Recommendation Systems.
It combines information extraction and generation techniques to produce summaries of reviews of individual songs. We conducted an intrinsic evaluation of the extraction components, and of the informativeness of the summaries; and a user study of the impact of the song review summaries on users’ decision making processes. Users were able to make quicker and more informed decisions when presented with the summary as compared to the full album review.
Barbara Di Eugenio is Associate Professor in the Department of Computer Science of the University of Illinois, Chicago campus. There she leads the NLP laboratory (http://nlp.cs.uic.edu/).
She obtained her laurea in Informatica in 1985, from Universita' di Torino, and her PhD in Computer Science from the University of Pennsylvania. She is an NSF CAREER awardee, and a past treasurer of the North American Chapter of the Association for Computational Linguistics.
Area di ricerca:
Metodologie e architetture software avanzate