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Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.

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Abstract. Opinion mining is a sub-discipline of computational linguistics that uses information retrieval techniques in order to determine whether a piece of text expresses a positive, negative or neutral opinion. In this paper, we present an approach for the opinion mining of financial news through the process of identifying their semantic polarity. Our approach relies on an algorithm that combines several gazetteer lists and leverages an existing financial ontology. The financialrelated news are obtained from RSS feeds and then automatically annotated with positive or negative markers. The outcome of the process is a set of news organized by their degree of positivity and negativity. The preliminary experimental results seem promising as compared against traditional approaches.

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