@InProceedings{janke-EtAl:2018:W18-32,
  author    = {Janke, Florian  and  Li, Tongrui  and  Rincón, Eric  and  Guzmán, Gualberto  and  Bullock, Barbara  and  Toribio, Almeida Jacqueline},
  title     = {The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018},
  booktitle = {Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-3216}
}