@inproceedings{janke-etal-2018-university,
title = "The {U}niversity of {T}exas System Submission for the Code-Switching Workshop Shared Task 2018",
author = "Janke, Florian and
Li, Tongrui and
Rinc{\'o}n, Eric and
Guzm{\'a}n, Gualberto and
Bullock, Barbara and
Toribio, Almeida Jacqueline",
editor = "Aguilar, Gustavo and
AlGhamdi, Fahad and
Soto, Victor and
Solorio, Thamar and
Diab, Mona and
Hirschberg, Julia",
booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3216",
doi = "10.18653/v1/W18-3216",
pages = "120--125",
abstract = "This paper describes the system for the Named Entity Recognition Shared Task of the Third Workshop on Computational Approaches to Linguistic Code-Switching (CALCS) submitted by the Bilingual Annotations Tasks (BATs) research group of the University of Texas. Our system uses several features to train a Conditional Random Field (CRF) model for classifying input words as Named Entities (NEs) using the Inside-Outside-Beginning (IOB) tagging scheme. We participated in the Modern Standard Arabic-Egyptian Arabic (MSA-EGY) and English-Spanish (ENG-SPA) tasks, achieving weighted average F-scores of 65.62 and 54.16 respectively. We also describe the performance of a deep neural network (NN) trained on a subset of the CRF features, which did not surpass CRF performance.",
}
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%0 Conference Proceedings
%T The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018
%A Janke, Florian
%A Li, Tongrui
%A Rincón, Eric
%A Guzmán, Gualberto
%A Bullock, Barbara
%A Toribio, Almeida Jacqueline
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F janke-etal-2018-university
%X This paper describes the system for the Named Entity Recognition Shared Task of the Third Workshop on Computational Approaches to Linguistic Code-Switching (CALCS) submitted by the Bilingual Annotations Tasks (BATs) research group of the University of Texas. Our system uses several features to train a Conditional Random Field (CRF) model for classifying input words as Named Entities (NEs) using the Inside-Outside-Beginning (IOB) tagging scheme. We participated in the Modern Standard Arabic-Egyptian Arabic (MSA-EGY) and English-Spanish (ENG-SPA) tasks, achieving weighted average F-scores of 65.62 and 54.16 respectively. We also describe the performance of a deep neural network (NN) trained on a subset of the CRF features, which did not surpass CRF performance.
%R 10.18653/v1/W18-3216
%U https://aclanthology.org/W18-3216
%U https://doi.org/10.18653/v1/W18-3216
%P 120-125
Markdown (Informal)
[The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018](https://aclanthology.org/W18-3216) (Janke et al., ACL 2018)
ACL