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Linked Open Tafsir - Rekonstruktion der Entstehungsdynamik(en) des Korans mithilfe der Netzwerkmodellierung früher islamischer Überlieferungen ...
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Linked Open Tafsir - Rekonstruktion der Entstehungsdynamik(en) des Korans mithilfe der Netzwerkmodellierung früher islamischer Überlieferungen ...
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EMBEDDIA tools output example corpus of Estonian, Croatian and Latvian news articles 1.0
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Measuring Semantic Similarity of Documents by Using Named Entity Recognition Methods
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In: Masters (2022)
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An RG-FLAT-CRF Model for Named Entity Recognition of Chinese Electronic Clinical Records
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In: Electronics; Volume 11; Issue 8; Pages: 1282 (2022)
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| 10 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition
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In: Sensors; Volume 22; Issue 8; Pages: 2852 (2022)
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A Pipeline Approach to Context-Aware Handwritten Text Recognition
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In: Applied Sciences; Volume 12; Issue 4; Pages: 1870 (2022)
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Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3885 (2022)
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Abstract:
Named entity recognition of forest diseases plays a key role in knowledge extraction in the field of forestry. The aim of this paper is to propose a named entity recognition method based on multi-feature embedding, a transformer encoder, a bi-gated recurrent unit (BiGRU), and conditional random fields (CRF). According to the characteristics of the forest disease corpus, several features are introduced here to improve the method’s accuracy. In this paper, we analyze the characteristics of forest disease texts; carry out pre-processing, labeling, and extraction of multiple features; and construct forest disease texts. In the input representation layer, the method integrates multi-features, such as characters, radicals, word boundaries, and parts of speech. Then, implicit features (e.g., sentence context features) are captured through the transformer’s encoding layer. The obtained features are transmitted to the BiGRU layer for further deep feature extraction. Finally, the CRF model is used to learn constraints and output the optimal annotation of disease names, damage sites, and drug entities in the forest disease texts. The experimental results on the self-built data set of forest disease texts show that the precision of the proposed method for entity recognition reached more than 93%, indicating that it can effectively solve the task of named entity recognition in forest disease texts.
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Keyword:
bi-gated recurrent unit; CRF; disease; multi-feature; named entity recognition; transformer
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URL: https://doi.org/10.3390/app12083885
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Learning the Morphological and Syntactic Grammars for Named Entity Recognition
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In: Information; Volume 13; Issue 2; Pages: 49 (2022)
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Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
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In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
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A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition
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In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022)
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An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
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In: Information; Volume 13; Issue 3; Pages: 137 (2022)
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StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Effect of depth order on iterative nested named entity recognition models
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In: Conference on Artificial Intelligence in Medecine (AIME 2021) ; https://hal.archives-ouvertes.fr/hal-03277643 ; Conference on Artificial Intelligence in Medecine (AIME 2021), Jun 2021, Porto, Portugal (2021)
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