Year: 2018
Type: conference-paper
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 Fscores 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.
DOI: 10.18653/v1/w18-3216
Authors: Florian Janke, Tongrui Li, Eric Rincón, Gualberto Guzmán, Barbara Bullock, Almeida Jacqueline Toribio
Institution: The University of Texas at Austin
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2885028086