Research lines

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.

Scientific text mining

Scientists worldwide are confronted with an exponential growth in the number of scientific documents, All this unprecedented volume of information complicates the task of researchers who are faced with the pressure of keeping up-to-date with discoveries in their own disciplines and with the challenge of searching for innovation, new interesting problems to solve, checking already solved problems or hypothesis, or getting information on past and current available methods, solutions or techniques.  Our laboratory works in the areas of document analysis, scientific summarization, argument mining in scientific literature, and information extraction from scientific texts.

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.

Sign Language processing

In LaTSUS lab, we are working on using state-of-the-art machine learning techniques in text generation, text simplification, and natural language understanding to aid machine translation between signed and spoken languages. We are contributing to the EU-wide project SignON which will provide a translation interface for deaf and hard-of-hearing people.