ACE-ADP: Adversarial Contextual Embeddings Based Named Entity Recognition for Agricultural Diseases and Pests

Xuchao Guo, Xia Hao, Zhan Tang, Lei Diao, Zhao Bai, Shuhan Lu and Lin Li
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Xuchao Guo: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xia Hao: College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000, China
Zhan Tang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Lei Diao: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Zhao Bai: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Shuhan Lu: School of Information, University of Michigan, Ann Arbor, MI 48104, USA
Lin Li: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2021, vol. 11, issue 10, 1-20

Abstract: Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of the text representation needs to be further enhanced, (3) the performance for rare entities needs to be further improved. We proposed an adversarial contextual embeddings-based model named ACE-ADP for named entity recognition in Chinese agricultural diseases and pests domain (CNER-ADP). First, we enhanced the text representation and overcame the polysemy problem by using the fine-tuned BERT model to generate the contextual character-level embedded representation with the specific knowledge. Second, adversarial training was also introduced to enhance the generalization and robustness in terms of identifying the rare entities. The experimental results showed that our model achieved an F 1 of 98.31% with 4.23% relative improvement compared to the baseline model (i.e., word2vec-based BiLSTM-CRF) on the self-annotated corpus named Chinese named entity recognition dataset for agricultural diseases and pests (AgCNER). Besides, the ablation study and discussion demonstrated that ACE-ADP could not only effectively extract rare entities but also maintain a powerful ability to predict new entities in new datasets with high accuracy. It could be used as a basis for further research on other domain-specific named entity recognition.

Keywords: digital agriculture; Chinese agricultural diseases and pests; named entity recognition; adversarial training; semantic enhancement (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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