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Attempto Controlled English (ACE) is a knowledge representation language with an English syntax. Thus ACE can be used by anyone, even without being familiar with formal notations. The At-tempto Parsing Engine translates ACE texts into discourse representation structures, a variant of first-order logic. Hence, ACE turns out to be a logic language equivalent(More)
This paper presents a three-level structuring of multiword terms (MWTs) basing on lexical inclusion, WordNet similarity and a clustering approach. Term clustering by automatic data analysis methods offers an interesting way of organizing a domain's knowledge structures, useful for several information-oriented tasks like science and technology watch,(More)
OBJECTIVE The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches(More)
We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a handwritten competence grammar with proba-bilistic performance disambiguation and that has been used in the biomedical domain. We discuss its performance in the domain adaptation open submission. We achieve average results, which is partly due to difficulties in mapping to the(More)
Successfully managing information means being able to find relevant new information and to correctly integrate it with pre-existing knowledge. Much information is nowadays stored as multilingual textual data; therefore advanced classification systems are currently considered as strategic components for effective knowledge management. We describe an(More)
We describe a system for the detection of mentions of protein-protein interactions in the biomedical scientific literature. The original system was developed as a part of the OntoGene project, which focuses on using advanced computational linguistic techniques for text mining applications in the biomedical domain. In this paper, we focus in particular on(More)
This paper describes an advanced system for multilingual text classification adaptable to different user needs. The system has been initially developed as an applied research project involving both research centres, industrial bodies and end-user organizations. The project is a considerable success story in the financial field. Three different successful(More)
We describe the task of automatically detecting interactions between proteins in biomedical literature. We use a syntactic parser, a corpus annotated for proteins, and manual decisions as training material. After automatically parsing the GENIA corpus, which is manually annotated for proteins, all syntactic paths between proteins are extracted. These(More)