Horacio Saggion, Head of the Lab
Francesco Ronzano, Senior Researcher
Daniel Ferrés, Research Assistant
Francesco Barbieri, PhD Candidate
Luis Espinosa-Anke, PhD Candidate
Ahmed Abura'ed, PhD Candidate
Pablo Accuosto, PhD Candidate
Automatic Text Summarization and Abstracting
We develop robust multilingual single-document and multi-document summarization technology. We have a set of resources to create summarization systems adapted to different needs and domains, this is being developed in the SUMMA system. Other areas of research we are working on are abstractive text summarization and summarization evaluation.
Automatic Text Simplification
Text simplification is the process of transforming a text into an equivalent which is more understandable for a target population. Simplified texts are appropriate for many groups of readers, such as language learners, elderly persons and people with other special reading and comprehension necessities. In TALN we develop robust natural language processing technology to produce simplified versions of documents at both the syntactic and lexical levels.
Sentiment Analysis and Opinion Mining
We work on multilingual (English/Spanish) sentiment analysis using lexical resources focusing on the use of machine learning technology over linguistic and semantic features for classification of opinionated texts. A new line of research is addressing the issue of irony identification in social networks and extending the irony detection model to humor and sarcasm.