Delimitação e alinhamento de conceitos lexicalizados no inglês norte-americano e no português brasileiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Enfrentamento do problema das divergências de tradução por um sistema de tradução automática: um exercício exploratório

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

As abordagens teóricas e os formalismos para o tratamento computacional do significado lexical

No âmbito do Processamento Automático de Línguas Naturais (PLN), o desenvolvimento de recursos léxico-semânticos é premente. Ao conceber os sistemas de PLN como um exercício de engenharia da linguagem humana, acredita-se que o desenvolvimento de tais recursos pode ser beneficiado pelos modelos de representação do conhecimento, desenvolvidos pela Engenharia do Conhecimento. Esses modelos, em particular, fornecem simultaneamente o arcabouço teórico-metodológico e a metalinguagem formal para o tratamento computacional do significado das unidades lexicais. Neste artigo, após a apresentação da concepção linguístico-computacional de léxico, elucidam-se os principais paradigmas de representação do conhecimento, enfatizando a abordagem do significado e a metalinguagem formal vinculadas a cada um deles.

O processamento automático de línguas naturais enquanto engenharia do conhecimento linguístico

In this paper, we provide a brief description of the multidisciplinary domain of research called Natural Language Processing (NLP), which aims at enabling the computer to deal with natural languages. In accordance with this description, NLP is conceived as "human language engineering or technology". Therefore, NLP requires consistent description of linguistic facts on every linguistic level: morphological, syntactic, semantic, and even the level of pragmatics and discourse. In addition to the linguistically-motivated conception of NLP, we emphasize the origin of such research field, the place occupied by NLP inside a multidisciplinary scenario, their objectives and challenges. Finally, we provide some remarks on the automatic processing of Brazilian Portuguese language.

Revisão sobre técnicas de controle em estruturas inteligentes

One of the great challenges of structural dynamics is to ally structures lighther and stronger. The great difficulty is that light systems, in general, have a low inherent damping. Besides, they contain resonance frequencies in the low frequency range. So, any external disturbance can excite the system in some resonance and the resulting effect can be drastic. The methodologies of active damping, with control algorithms and piezoelectric sensors and actuators coupled in a base structure, are attractive in current days, in order to overcome the contradictory features of these requeriments. In this sense, this article contributes with a bibliographical review of the literature on the importance of active noise and vibration control in engineering applications, models of smart structures, techniques of optimal placement of piezoelectric sensors and actuators and methodologies of structural active control. Finally, it is discussed the future perspectives in this area.

Construção exploratória de uma prepnet para o português do Brasil: uma incursão linguistico-computacional no universo das preposições indicativas de espaço

Pós-graduação em Linguística e Língua Portuguesa - FCLAR

Construção de um ontoléxico para o universo léxico-conceitual da indústria do bordado de Ibitinga

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A metáfora e a sua representação em sistemas de processamento automático de línguas naturais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelo linguístico-computacional para um dicionário analógico digital

Pós-graduação em Linguística e Língua Portuguesa - FCLAR

Construção linguístico-computacional de um ontoglossário inglês-português do domínio léxico-conceitual LOVE LIFE

Pós-graduação em Linguística e Língua Portuguesa - FCLAR

A interlíngua e o alinhamento léxico-conceitual da base de dados bilíngue REBECA

In the architecture of a natural language processing system based on linguistic knowledge, two types of component are important: the knowledge databases and the processing modules. One of the knowledge databases is the lexical database, which is responsible for providing the lexical unities and its properties to the processing modules. The systems that process two or more languages require bilingual and/or multilingual lexical databases. These databases can be constructed by aligning distinct monolingual databases. In this paper, we present the interlingua and the strategy of aligning the two monolingual databases in REBECA, which only stores concepts from the “wheeled vehicle” domain.

Deep learning architecture for gesture recognition

Pós-graduação em Engenharia Mecânica - FEG

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Power-law distribution in a learning process: competition, learning and natural selection

In the present work, we propose a model for the statistical distribution of people versus number of steps acquired by them in a learning process, based on competition, learning and natural selection. We consider that learning ability is normally distributed. We found that the number of people versus step acquired by them in a learning process is given through a power law. As competition, learning and selection is also at the core of all economical and social systems, we consider that power-law scaling is a quantitative description of this process in social systems. This gives an alternative thinking in holistic properties of complex systems. (C) 2004 Elsevier B.V. All rights reserved.