Christopher Shulby


2017

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Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks
Marcos Treviso | Christopher Shulby | Sandra Aluísio
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks in order to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech as well as normal, prepared speech and presents better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.

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Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
Nathan Hartmann | Erick Fonseca | Christopher Shulby | Marcos Treviso | Jéssica Silva | Sandra Aluísio
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology

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Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts
Marcos Treviso | Christopher Shulby | Sandra Aluísio
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology

2013

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Automatic Disambiguation of Homographic Heterophone Pairs Containing Open and Closed Mid Vowels
Christopher Shulby | Gustavo Mendonça | Vanessa Marquiafável
Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology