[Show abstract][Hide abstract] ABSTRACT: In the research framework in meaning representation in NLP, we focus our attention on thematic aspects and con- ceptual vectors. This vectorial base is built upon a mor- phosyntactic analysis of several lexical resources to reduce isolated problems. Also a meaning is a cluster of defini- tions that are pointed by an Id number. To check the results of an automatic clustering or a word sense disambiguation, we must continuously refer to the source dictionary. In this article, we describe a method for naming a word sense by a term of the vocabulary. This kind of annotation is a light and efficient method that uses meanings associations some- one or something can extract from any lexical knowledge base. Finally, the annotations should become a new lexical learning resource to improve the vectorial base.
[Show abstract][Hide abstract] ABSTRACT: The semantic analysis of texts requires beforehand the building of objects related to lexical semantics. Idea vectors and lexical networks seems to be adequate for such a purpose and are complementary. However, one should still be able to construct them in practice. Vectors can be computed with definition corpora extracted from dictionaries, with thesaurii or with plain texts. They can be derived as conceptual vectors, anonymous vectors or lexical vectors - each of those being a particular balance between precision, coverage and practicality. Concerning lexical networks, they can be efficiently constructed through serious games, which is precisely the goal of the JeuxDeMots project. The semantic analysis can be tackled from the thematic analysis, and can serve as computing means for idea vectors. We can modelise the analysis problem as actviations and propagations. The numerous criteria occuring in the semantic analysis and the difficulties related to the proper definition
of a control function, lead us to explore metaheuristics inspired from nature. More precisely, we introduce
an analysis moodel based on artificial ant colonies. From a given text, the analysis aims at building a graph holding objects of the text (words, phrases, sentences, etc.), highlighting objects considered as relevant (phrases and concepts) as well as typed and weighted relations between those objects.
Computer Science, French National Centre for Scientific Research, 12/2011, Degree: HDR
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