Reference and the facilitation of search in spatial domains

This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin

Generating Easy References : the case of document deixis.

This work is part of an ongoing PhD project of the first author and it has been supported by the CNPq, the Brazilian Research Council.Publisher PD

Using \u27Low-cost\u27 Learning Features for Pronoun Resolution

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Um Sistema de Realização Superficial para Geração de Textos em Português

Sistemas de geração de língua natural (GLN) - que produzem texto a partir de dados não-linguísticos - possuem uma ampla gama de aplicações em visualização textual de conteúdos complexos e/ou em grandes volumes. Este trabalho enfoca a implementação de um módulo de realização textual baseado em regras para o português brasileiro, chamado PortNLG, que trata da tarefa de linearização sentencial para aplicações computacionais que necessitem apresentar dados de saída em formato textual. PortNLG é apresentado na forma de uma biblioteca JAVA, e seus resultados são superiores aos de modelos de n-gramas na tarefa de geração de manchetes de jornal

Generating Easy References : the case of document deixis.

This work is part of an ongoing PhD project of the first author and it has been supported by the CNPq, the Brazilian Research Council.Publisher PD

Myers-Briggs personality classification from social media text using pre-trained language models

In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these methods for personality classification from text. Focusing on the Myers-Briggs (MBTI) personality model, we describe a series of experiments in which the well-known Bidirectional Encoder Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike across multiple evaluation scenarios, and generally outperforms previous work in the field