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20062021
most citedPortuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

91 citations · 154 across the 6 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.CL2017★ 91 cited

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

Nathan Hartmann, Erick Fonseca, Christopher Shulby +3

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems. In…

cs.CL2017★ 13 cited

Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts

Marcos V. Treviso, Christopher D. Shulby, Sandra M. Aluisio

This paper is motivated by the automation of neuropsychological tests involving discourse analysis in the retellings of narratives by patients with potential cognitive impairment.…

cs.SD2017

Acoustic Modeling Using a Shallow CNN-HTSVM Architecture

Christopher Dane Shulby, Martha Dais Ferreira, Rodrigo F. de Mello +1

High-accuracy speech recognition is especially challenging when large datasets are not available. It is possible to bridge this gap with careful and knowledge-driven parsing combin…

cs.CL2017★ 1 cited

A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese

Leandro B. dos Santos, Magali S. Duran, Nathan S. Hartmann +3

Psycholinguistic properties of words have been used in various approaches to Natural Language Processing tasks, such as text simplification and readability assessment. Most of thes…

cs.CL2017

Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts

Leandro B. dos Santos, Edilson A. Corrêa, Osvaldo N. Oliveira +3

Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for lar…

cs.CL2017

Automatic semantic role labeling on non-revised syntactic trees of journalistic texts

Nathan Siegle Hartmann, Magali Sanches Duran, Sandra Maria Aluísio

Semantic Role Labeling (SRL) is a Natural Language Processing task that enables the detection of events described in sentences and the participants of these events. For Brazilian P…