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

Abstract

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

Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition

“…This approach incorporates all the words corresponding to each character within the representation layer and can be employed in different sequence labeling frameworks. In the field of agriculture, to alleviate the problems of agricultural text professionalism and uneven distribution of entity types, Zhang et al [34] combined Soft-Lexicon with an attention mechanism and proposed the AttSoftlexicon to help the model effectively utilize lexical information. Building upon prior research, we integrate the Soft-Lexicon approach with RoBERTa and utilize the traditional BiLSTM-CRF architecture to achieve the intelligent extraction of entities from Chinese electronic medical records.…”

GlyReShot: A glyph-aware model with label refinement for few-shot Chinese agricultural named entity recognition

“…Guo et al [6] learn contextual glyph information from the image perspective with 3-dimension CNN to integrate morphological knowledge. Due to the absence of word information in character-based Chinese agricultural NER models, some researches incorporate lexicon information into their models [5] , [24] , [25] . Furthermore, we also notice that Liang et al [26] study Chinese agricultural NER with small samples.…”