Chinese named entity recognition for apple diseases and pests based on character augmentation

Zhang, Jiayu; Guo, Mei; Geng, Yaojun; Li, Mei; Zhang, Yongliang; Geng, Nan

doi:10.1016/j.compag.2021.106464

Cited by 15 publications

(4 citation statements)

References 13 publications

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“…For different fields, entity recognition research will be additional. Zhang et al [5] proposed a Chinese character-based enhancement NER model. It aimed at the problems of Chinese NER in apple diseases and insect pests, including many types of entities, entities with aliases or abbreviations, and difficulties in identifying rare entities.…”

Section: Literature Reviewmentioning

confidence: 99%

Legal Text Recognition Using LSTM-CRF Deep Learning Model

2022

Computational Intelligence and Neuroscience

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In legal texts, named entity recognition (NER) is researched using deep learning models. First, the bidirectional (Bi)-long short-term memory (LSTM)-conditional random field (CRF) model for studying NER in legal texts is established. Second, different annotation methods are used to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. Finally, other objective loss functions are set to compare and analyze the entity recognition effect of the Bi-LSTM-CRF model. The research results show that the F1 value of the model trained on the word sequence labeling corpus on the named entity is 88.13%, higher than that of the word sequence labeling corpus. For the two types of entities, place names and organization names, the F1 values obtained by the Bi-LSTM-CRF model using word segmentation are 67.60% and 89.45%, respectively, higher than the F1 values obtained by the model using character segmentation. Therefore, the Bi-LSTM-CRF model using word segmentation is more suitable for recognizing extended entities. The parameter learning result using log-likelihood is better than that using the maximum interval criterion, and it is ideal for the Bi-LSTM-CRF model. This method provides ideas for the research of legal text recognition and has a particular value.

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Section: Literature Reviewmentioning

confidence: 99%

Legal Text Recognition Using LSTM-CRF Deep Learning Model

2022

Computational Intelligence and Neuroscience

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“…However, it is difficult for the method to identify entities, such as fertilizers, crop varieties, and weeds. Zhang et al (2021) [10] proposed a Chinese named entity recognition model based on character enhancement for Chinese named entity recognition in apple diseases and pests. Dictionaries and similar words were incorporated into the character-based BiLSTM-CRF model to enhance character representation.…”

Section: Related Workmentioning

confidence: 99%

Agricultural text named entity recognition based on the BiLSTM-CRF model

Qian

Chen

Wang

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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Named entity recognition (NER) is an essential part of many natural language processing applications and serves as the basis of natural language processing. NER tasks aim to identify the entity boundaries and types of specific meanings in texts, which include person names, place names, organizations, and dates. Due to the complexity of the composition of agricultural entities and limited quantity of agricultural corpora, often with low quality, agricultural text named entity recognition (ANER) is a more difficult and challenging problem compared to general NER tasks. Existing models often have poor performance in ANER tasks. To solve the existing problems of ANER, we constructed a named entity dataset of agricultural context through manual annotation using collected massive agricultural texts. The dataset contains 8873 entities composed of six entity categories: crop type, disease type, pest type, pesticide type, fertilizer type, and crop part type. We propose an agricultural text named entity recognition model based on BiLSTM-CRF. Experimental results show that the F1 score of the model on the test set achieved 91.20%, and our model also achieved the highest precision of 93.23% and the highest recall of 89.39% compared to other existing models.

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“…We obtained the public data according to Table 2 in study (Liu et al, 2022). This study randomly divided KIWID, BOSON, and ClueNER into training, validation, and test sets according to a ratio of 8:1:1, respectively [refer to Zhang et al (2021)]. Division of People's Daily reference https://github.com/zjyucas/ChineseNER.…”

Section: Dataset Divisionmentioning

confidence: 99%

Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach

et al. 2022

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Named Entity Recognition (NER) is a crucial step in mining information from massive agricultural texts, which is required in the construction of many knowledge-based agricultural support systems, such as agricultural technology question answering systems. The vital domain characteristics of Chinese agricultural text cause the Chinese NER (CNER) in kiwifruit diseases and pests to suffer from the insensitivity of common word segmentation tools to kiwifruit-related texts and the feature extraction capability of the sequence encoding layer being challenged. In order to alleviate the above problems, effectively mine information from kiwifruit-related texts to provide support for agricultural support systems such as agricultural question answering systems, this study constructed a novel Chinese agricultural NER (CANER) model KIWINER by statistics-based new word detection and two novel modules, AttSoftlexicon (Criss-cross attention-based Softlexicon) and PCAT (Parallel connection criss-cross attention), proposed in this paper. Specifically, new words were detected to improve the adaptability of word segmentation tools to kiwifruit-related texts, thereby constructing a kiwifruit lexicon. The AttSoftlexicon integrates word information into the model and makes full use of the word information with the help of Criss-cross attention network (CCNet). And the PCAT improves the feature extraction ability of sequence encoding layer through CCNet and parallel connection structure. The performance of KIWINER was evaluated on four datasets, namely KIWID (Self-annotated), Boson, ClueNER, and People’s Daily, which achieved optimal F1-scores of 88.94%, 85.13%, 80.52%, and 92.82%, respectively. Experimental results in many aspects illustrated that methods proposed in this paper can effectively improve the recognition effect of kiwifruit diseases and pests named entities, especially for diseases and pests with strong domain characteristics

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Chinese named entity recognition for apple diseases and pests based on character augmentation

Cited by 15 publications

References 13 publications

Legal Text Recognition Using LSTM-CRF Deep Learning Model

Legal Text Recognition Using LSTM-CRF Deep Learning Model

Agricultural text named entity recognition based on the BiLSTM-CRF model

Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach

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