Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.Fapesp - Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil[2008/08245-4

TÉCNICAS DE PROCESSAMENTO DE LINGUAGEM NATURAL APLICADAS AO PROCESSO DE MINERAÇÃO DE TEXTOS: RESULTADOS PRELIMINARES DE UM MAPEAMENTO SISTEMÁTICO

Text mining is an activity that aims to discover knowledge in not-structured data (textual. This process uses itself algorithms as well as known and consolidated techniques, among which can be termed Natural Language Processing (NLP) which has incremented obtained results and has justified the necessary computational effort. Objective: The aim of this study was to identify and evaluate the techniques of NLP available to perform data mining in textual databases. Method: We applied a systematic mapping study to identify, evaluate and interpret relevant studies about this research topic. Results: We identify 24 papers discussing about 11 NLP techniques applied in text mining, in which the ontology was presented as the most efficient technique throughout the years.A mineração de textos é a atividade que surgiu com o propósito de descobrir conhecimento em dados não estruturados (textuais). Este processo utiliza além de algoritmos próprios, técnicas já conhecidas e consolidadas, dentre elas o Processamento de Linguagem Natural (PLN) tem incrementado os resultados obtidos. Objetivo: Este estudo teve como objetivo identificar e avaliar as técnicas de PLN disponíveis para realizar mineração em bases de dados textuais com o intuito de discutir sobre essas técnicas a partir das experiências publicadas neste contexto. Método: Foi utilizada a técnica de mapeamento sistemático, cujo propósito é identificar, avaliar e interpretar estudos disponíveis e relevantes sobre uma determinada questão de pesquisa, executando um processo de revisão rigoroso e confiável. Resultados: Foram analisados 24 estudos aplicando 11 técnicas diferentes de PLN na mineração de textos, sendo que dentre todas essas técnicas, a ontologia se mostrou a mais recorrente e eficiente.