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Authors: Ermelinda Oro 1 ; Massimo Ruffolo 1 and Mostafa Sheikhalishahi 2

Affiliations: 1 National Research Council (CNR), Italy ; 2 Fondazione Bruno Kessler, Italy

ISBN: 978-989-758-275-2

Keyword(s): Language Identification, Word Embedding, Natural Language Processing, Deep Neural Network, Long Short-Term Memory, Recurrent Neural Network.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Natural Language Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: The goal of similar Language IDentification (LID) is to quickly and accurately identify the language of the text. It plays an important role in several Natural Language Processing (NLP) applications where it is frequently used as a pre-processing technique. For example, information retrieval systems use LID as a filtering technique to provide users with documents written only in a given language. Although different approaches to this problem have been proposed, similar language identification, in particular applied to short texts, remains a challenging task in NLP. In this paper, a method that combines word vectors representation and Long Short-Term Memory (LSTM) has been implemented. The experimental evaluation on public and well-known datasets has shown that the proposed method improves accuracy and precision of language identification tasks.

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Paper citation in several formats:
Oro E., Ruffolo M. and Sheikhalishahi M. (2018). Language Identification of Similar Languages using Recurrent Neural Networks.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 635-640. DOI: 10.5220/0006678606350640

@conference{icaart18,
author={Ermelinda Oro and Massimo Ruffolo and Mostafa Sheikhalishahi},
title={Language Identification of Similar Languages using Recurrent Neural Networks},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={635-640},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006678606350640},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Language Identification of Similar Languages using Recurrent Neural Networks
SN - 978-989-758-275-2
AU - Oro E.
AU - Ruffolo M.
AU - Sheikhalishahi M.
PY - 2018
SP - 635
EP - 640
DO - 10.5220/0006678606350640

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