I have focused my research on information
extraction, retrieval and organization from text documents, using several machine learning (probabilistic
and statistical) approaches.
In these years, I have analyzed different
aspects of text/web mining moving from classical text problems as
supervised and unsupervised learning, or new representation for
text document to more computational linguistic tasks, as statistical machine translation and document summarization.
I have been part of the European project SMART,
where I have applied machine learning techniques to statistical machine translation (SMT)
problems, and I have been also involved in a media analysis project aimed at
modeling the mediasphere based on text mining and cross-language
current research is centered on SMT techniques applied to news domain, in particular, on the use of translated documents in
different NLP tasks such as document summarization, event extraction
and sentiment analysis.
also interested on multilingual multi-label document
classification, robust approaches for outliers detection in text
mining, multilingual patterns learning and news analysis.
last works involve development of a translation service of news, learning curves analysis of a SMT system, use of machine translation to directly and indirectly address multilingual text mining and document summarization.