ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43957163" target="_blank" >RIV/49777513:23520/19:43957163 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.18653/v1/W19-4623" target="_blank" >http://dx.doi.org/10.18653/v1/W19-4623</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/W19-4623" target="_blank" >10.18653/v1/W19-4623</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects

Popis výsledku v původním jazyce

In this paper, we present our systems for the MADAR Shared Task: Arabic Fine-Grained Dialect Identification. The shared task consists of two subtasks. The goal of Subtask–1 (S-1) is to detect an Arabic city dialect in a given text and the goal of Subtask–2 (S-2) is to predict the country of origin of a Twitter user by using tweets posted by the user. In S-1, our proposed systems are based on language modelling. We use language models to extract features that are later used as an input for other machine learning algorithms. We also experiment with recurrent neural networks (RNN), but these experiments showed that simpler machine learning algorithms are more successful. Our system achieves 0.658 macro F1-score and our rank is 6 th out of 19 teams in S-1 and 7 th in S-2 with 0.475 macro F1-score.

Název v anglickém jazyce

ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects

Popis výsledku anglicky

In this paper, we present our systems for the MADAR Shared Task: Arabic Fine-Grained Dialect Identification. The shared task consists of two subtasks. The goal of Subtask–1 (S-1) is to detect an Arabic city dialect in a given text and the goal of Subtask–2 (S-2) is to predict the country of origin of a Twitter user by using tweets posted by the user. In S-1, our proposed systems are based on language modelling. We use language models to extract features that are later used as an input for other machine learning algorithms. We also experiment with recurrent neural networks (RNN), but these experiments showed that simpler machine learning algorithms are more successful. Our system achieves 0.658 macro F1-score and our rank is 6 th out of 19 teams in S-1 and 7 th in S-2 with 0.475 macro F1-score.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů