José Saias

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This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each(More)
Web legal information retrieval systems need the capability to reason with the knowledge modelled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The(More)
The University ofÉvora team in QA@CLEF-2007 tested their Senso system in the Por-tuguese monolingual task. The system uses an ontology semantic information for text search terms expansion and for verification of concept equivalency or IsA/specialization relations. The full text collection is indexed and for each question it's done a search, for retrieval of(More)
Modern information retrieval systems need the capability to reason about the knowledge conveyed by text bases. In this paper a methodology to automatically create ontologies and class instances from documents is proposed. The ontology is defined in the OWL semantic web language and it is used by a logic programming framework, ISCO, to allow users to query(More)
Legal web information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology(More)
The approach followed by the University ofÉvora team in order to build a system able to participate in the QA-CLEF task is described. The system is based in two steps: for each question, a first information retrieval task selects a set of potentially relevant documents; then, each of these documents is analysed trying to obtain their semantic representation(More)