The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018
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.
Code (0)
등록된 구현이 없습니다.
Tasks
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Single Particle AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Howard University System Submission for the Shared Task in Language Identification in Spanish-English Codeswitching
A University of Texas Medical Branch Case Study on Aortic Calcification Detection
This case study details The University of Texas Medical Branch (UTMB)'s partnership with Zauron Labs, Inc. to enhance detection and coding of aortic calcifications (ACs) using chest radiographs. ACs are often underreport…
The Samsung and University of Edinburgh’s submission to IWSLT17
This paper describes the joint submission of Samsung Research and Development, Warsaw, Poland and the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in two translation directions, en-de and…
Decoderde-enDomain AdaptationTranslationThe University of Edinburgh’s systems submission to the MT task at IWSLT
This paper describes the submission of the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in four translation directions, en-de, de-en, en-fr, and fr-en. The models have been trained with a…
Decoderde-enDomain Adaptationfr-en+1