no code implementations • LREC 2020 • Wesley Santos, Am Funabashi, a, Iv Paraboni, r{\'e}
Depression and related mental health issues are often reflected in the language employed by the individuals who suffer from these conditions and, accordingly, research in Natural Language Processing (NLP) and related fields have developed an increasing number of studies devoted to their recognition in social media text.
no code implementations • LREC 2020 • Rafael Dias, Iv Paraboni, r{\'e}
Author profiling models predict demographic characteristics of a target author based on the text that they have written.
no code implementations • RANLP 2019 • Pablo Costa, Iv Paraboni, r{\'e}
In Natural Language Generation systems, personalization strategies - i. e, the use of information about a target author to generate text that (more) closely resembles human-produced language - have long been applied to improve results.
no code implementations • RANLP 2019 • Wesley Santos, Iv Paraboni, r{\'e}
We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks.
no code implementations • WS 2017 • Thiago Castro Ferreira, Iv Paraboni, r{\'e}
Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly.
no code implementations • CL 2017 • Iv Paraboni, r{\'e}, Alex Gwo Jen Lan, Matheus Mendes de Sant{'}Ana, Fl{\'a}vio Luiz Coutinho
Studies in referring expression generation (REG) have shown different effects of referential overspecification on the resolution of certain descriptions.
no code implementations • LREC 2012 • Hilder Pereira, Eder Novais, Andr{\'e} Mariotti, Iv Paraboni, r{\'e}
In Natural Language Generation, the task of attribute selection (AS) consists of determining the appropriate attribute-value pairs (or semantic properties) that represent the contents of a referring expression.
no code implementations • LREC 2012 • Eder Novais, Iv Paraboni, r{\'e}, Douglas Silva
Among these, there is the issue of data sparseness, a problem that is particularly evident in cases such as our target language - Brazilian Portuguese - which is not only morphologically-rich, but relatively poor in NLP resources such as large, publicly available corpora.