Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach

In digital agriculture, agronomists are required to make timely, profitable and more actionable precise decisions based on knowledge and experience. The input can be cultivated and related agricultural data, and one of them is text data, including news articles, business news, policy documents, or f...

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Auteurs principaux:	Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac
Format:	article
Langue:	EN
Publié:	IEEE 2021
Sujets:	Agriculture entity recognition WordNet semantic class named entity recognition deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Accès en ligne:	https://doaj.org/article/51e7f07da2f24625884509f4e50289ef
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id	oai:doaj.org-article:51e7f07da2f24625884509f4e50289ef
record_format	dspace
spelling	oai:doaj.org-article:51e7f07da2f24625884509f4e50289ef2021-11-20T00:02:24ZDomain Specific Entity Recognition With Semantic-Based Deep Learning Approach2169-353610.1109/ACCESS.2021.3128178https://doaj.org/article/51e7f07da2f24625884509f4e50289ef2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615052/https://doaj.org/toc/2169-3536In digital agriculture, agronomists are required to make timely, profitable and more actionable precise decisions based on knowledge and experience. The input can be cultivated and related agricultural data, and one of them is text data, including news articles, business news, policy documents, or farming notes. To process this kind of data, identifying agricultural entities in the text is necessary to update news with agricultural orientation. This task is called Agriculture Entity Recognition (AGER - a kind of Named Entity Recognition task, NER, in the agriculture domain). However, there are very few approaches on AGER because of a lack of the consistent tagset and resources. In this study, we developed a new tagset for AGER to cover popular concepts in agriculture and we also propose a process for this task that consists of two stages: in the first stage, we use semantic-based approaches for detecting agricultural entities and semi-automatically build an annotated corpus of agricultural entities, while in the second stage, we identify the agricultural entities from the plain text using a deep learning approach, train on the annotated corpus. For the evaluation and validation, we build an annotated agriculture corpus and demonstrated the efficiency and robustness of our approach.Quoc Hung NgoTahar KechadiNhien-An Le-KhacIEEEarticleAgriculture entity recognitionWordNetsemantic classnamed entity recognitiondeep learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152892-152902 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Agriculture entity recognition WordNet semantic class named entity recognition deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Agriculture entity recognition WordNet semantic class named entity recognition deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Quoc Hung Ngo Tahar Kechadi Nhien-An Le-Khac Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
description	In digital agriculture, agronomists are required to make timely, profitable and more actionable precise decisions based on knowledge and experience. The input can be cultivated and related agricultural data, and one of them is text data, including news articles, business news, policy documents, or farming notes. To process this kind of data, identifying agricultural entities in the text is necessary to update news with agricultural orientation. This task is called Agriculture Entity Recognition (AGER - a kind of Named Entity Recognition task, NER, in the agriculture domain). However, there are very few approaches on AGER because of a lack of the consistent tagset and resources. In this study, we developed a new tagset for AGER to cover popular concepts in agriculture and we also propose a process for this task that consists of two stages: in the first stage, we use semantic-based approaches for detecting agricultural entities and semi-automatically build an annotated corpus of agricultural entities, while in the second stage, we identify the agricultural entities from the plain text using a deep learning approach, train on the annotated corpus. For the evaluation and validation, we build an annotated agriculture corpus and demonstrated the efficiency and robustness of our approach.
format	article
author	Quoc Hung Ngo Tahar Kechadi Nhien-An Le-Khac
author_facet	Quoc Hung Ngo Tahar Kechadi Nhien-An Le-Khac
author_sort	Quoc Hung Ngo
title	Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
title_short	Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
title_full	Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
title_fullStr	Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
title_full_unstemmed	Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach
title_sort	domain specific entity recognition with semantic-based deep learning approach
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/51e7f07da2f24625884509f4e50289ef
work_keys_str_mv	AT quochungngo domainspecificentityrecognitionwithsemanticbaseddeeplearningapproach AT taharkechadi domainspecificentityrecognitionwithsemanticbaseddeeplearningapproach AT nhienanlekhac domainspecificentityrecognitionwithsemanticbaseddeeplearningapproach
_version_	1718419831337779200

Domain Specific Entity Recognition With Semantic-Based Deep Learning Approach

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