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Possui graduação em Letras pela Universidade Federal de Minas Gerais (1987), mestrado em Linguística pela Universidade Federal de Minas Gerais (1990) e pela City University of New York (1992), e doutorado (1997) e pós-doutorado (1998) em Linguística pela City University of New York. Atualmente é Professor Associado da Universidade Federal de Minas Gerais.

A literatura apresenta muitas definições acerca da noção de Tópicoe de estrutura informacional(cf.BARBOSA, 2005; AUTOR, 2015). Neste trabalho, assume-sea definição daLanguageintoActTheory(CRESTI, 2000)que dizque o Tópico é a porção textual que se realiza por meio de umpadrãoentoacionaldotipoprefix(‘t HART et al. 1990)e que tem por função constituir o domínio sobre o qual a força ilocucionária se aplica. Um SN em Tópico pode ser o sujeito ou um anacoluto do verbo no Comentário.SNsanacolutos são sintagmas que não possuem relação sintática com o conteúdo presente no Comentário. Nestetrabalho, mostra-se comoSNsestão distribuídos probabilisticamente entre essas duas condições quando são realizados como Tópico na fala espontânea. Para isso, foram coletados dados decorporade fala espontânea etiquetadosinformacionalmente–incluindo a unidade de Tópico conforme definida acima–de três línguas: espanhol europeu(NICOLÁS MARTÍNEZ; LOMBÁN SOMACARRERA, 2018), inglês americano(CAVALCANTE; RAMOS, 2016)e português brasileiro(PANUNZI; MITTMANN, 2014). O método estatístico utilizado para o cálculo da probabilidade foi um modelo misto de regressão logística com efeitos aleatórios cruzados, conduzido com auxílio do R (R CORE TEAM, 2018). Três variáveis foram selecionadas para o cálculo: acessibilidade do referente,animacidadeedefinitude. O modelo mostrou que há cerca de cinco vezes mais chances de que o SNrealizado em Tópicoseja sujeito do verbo noComentário caso ele seja animado, definido e seja classificado como informação dada no discurso. The definition of Topicas well as that ofinformation structurein the literature is very broad(cf. BARBOSA, 2005; AUTHOR, 2015).Here we assume the definitionas proposed by the Language into Act Theory(CRESTI, 2000), which says that Topicis the textualunitthat is performed by an intonational profile of the prefix type(‘t HART et al. 1990),and that has the function of constituting the domain over which the illocutionary forceapplies.An NP in Topiceithercan be the subject or an anacoluthon of the following verb in Comment.Anacolutha NPs are phrases thatbearno syntactic relations with thepredicationin Comment.In this paper, we show how NPs are distributed probabilisticallybetween these two conditionswhenthey areperformed as Topics in spontaneous speech.For this purpose, we collected data fromavailablespontaneous speech corpora informationally labeled – including the Topic unit as defined above – from three languages: European Spanish(NICOLÁS MARTÍNEZ; LOMBÁN SOMACARRERA, 2018), American English(CAVALCANTE; RAMOS, 2016)and Brazilian Portuguese(PANUNZI; MITTMANN, 2014). The statistical method used to calculate the probability was a mixed-effects logistic regression with crossed random effectsconducted with the aid of R (R CORE TEAM, 2018). Three variables were chosen: accessibility of referent, animacy and definiteness. The model showedthat there are about five times more chancesforan NPperformed in Topicto be the subject of the verb in Comment if itis animate, definite and given.&nbsp

This paper presents a distributed platform for Natural Language Processing called PyPLN. PyPLN leverages a vast array of NLP and text processing open source tools, managing the distribution of the workload on a variety of configurations: from a single server to a cluster of linux servers. PyPLN is developed using Python 2.7.3 but makes it very easy to incorporate other softwares for specific tasks as long as a linux version is available. PyPLN facilitates analyses both at document and corpus level, simplifying management and publication of corpora and analytical results through an easy to use web interface. In the current (beta) release, it supports English and Portuguese languages with support to other languages planned for future releases. To support the Portuguese language PyPLN uses the PALAVRAS parser\citep{Bick2000}. Currently PyPLN offers the following features: Text extraction with encoding normalization (to UTF-8), part-of-speech tagging, token frequency, semantic annotation, n-gram extraction, word and sentence repertoire, and full-text search across corpora. The platform is licensed as GPL-v3.

The advances in the field of second (L2) and foreign (FL) language teaching and learning in the past two decades have been manifold, among these: acquisition theories that have emerged as a consequence of refinements in experimental and methodological tools; the shift of focus to approaches rather than methods in L2 and FL teaching; socio- interactionist studies’ emphasis on the ecology of the classroom, geared towards community building; and the questioning of teacher training paradigms by teacher development and education programs. The advances in the field of second (L2) and foreign (FL) language teaching and learning in the past two decades have been manifold, among these: acquisition theories that have emerged as a consequence of refinements in experimental and methodological tools; the shift of focus to approaches rather than methods in L2 and FL teaching; socio- interactionist studies’ emphasis on the ecology of the classroom, geared towards community building; and the questioning of teacher training paradigms by teacher development and education programs.