A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Wang, Haoriqin; Zhu, Haojin; Wu, Haiyuan; Wang, Xiaomin; Han, Xiao; Xu, Tongyu

doi:10.3390/agronomy11071307

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…The rice-related dataset was derived from the Q&A community of the China Agricultural Technology Extension Information Platform [20] in this paper. First, 5000 common rice-related questions were selected, classified into five categories, including 1519, 475, 1503, 376, and 1127 pairs, respectively, regarding diseases and pests, weeds, pesticides, cultivation management, storage and transportation, and OTHERS.…”

Section: Corpus Preparationmentioning

confidence: 99%

A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

Wang

et al. 2022

Agriculture

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To allow the intelligent detection of correct answers in the rice-related question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform,” we propose an answer selection model with dynamic attention and multi-strategy matching (DAMM). According to the characteristics of the rice-related dataset, the twelve-layer Chinese Bert pre-training model was employed to vectorize the text data and was compared with Word2vec, GloVe, and TF-IDF (Term Frequency–Inverse Document Frequency) methods. It was concluded that Bert could effectively solve the agricultural text’s high dimensionality and sparsity problems. As well as the problem of polysemy having different meanings in different contexts, dynamic attention with two different filtering strategies was used in the attention layer to effectively remove the sentence’s noise. The sentence representation of question-and-answer sentences was obtained. Secondly, two matching strategies (Full matching and Attentive matching) were introduced in the matching layer to complete the interaction between sentence vectors. Thirdly, a bi-directional gated recurrent unit (BiGRU) network spliced the sentence vectors obtained from the matching layer. Finally, a classifier was employed to calculate the similarity of splicing vectors, and the semantic correlation between question-and-answer sentences was acquired. The experimental results showed that DAMM had the best performance in the rice-related answer selection dataset compared with the other six answer selection models, of which MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) of DAMM gained 85.7% and 88.9%, respectively. Compared with the other six kinds of answer selection models, we present a new state-of-the-art method with the rice-related answer selection dataset.

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Section: Corpus Preparationmentioning

confidence: 99%

A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

Wang

et al. 2022

Agriculture

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“…More recently, Nguyen et al [ 8 ] outperformed previous studies on the SemEval data set by combining a convolutional neural network and features from external knowledge to measure the similarity between 2 questions. In addition to these studies on the aforementioned popular data sets, Wang et al [ 9 ] used a method based on the Coattention-DenseGRU (gated recurrent unit) to match similar questions on Chinese rice-related questions.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study

Saraswat¹,

li²,

Jiang³

2023

JMIR AI

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Background The regulatory affairs (RA) division in a pharmaceutical establishment is the point of contact between regulatory authorities and pharmaceutical companies. They are delegated the crucial and strenuous task of extracting and summarizing relevant information in the most meticulous manner from various search systems. An artificial intelligence (AI)–based intelligent search system that can significantly bring down the manual efforts in the existing processes of the RA department while maintaining and improving the quality of final outcomes is desirable. We proposed a “frequently asked questions” component and its utility in an AI-based intelligent search system in this paper. The scenario is further complicated by the lack of publicly available relevant data sets in the RA domain to train the machine learning models that can facilitate cognitive search systems for regulatory authorities. Objective In this study, we aimed to use AI-based intelligent computational models to automatically recognize semantically similar question pairs in the RA domain and evaluate the Recognizing Question Entailment–based system. Methods We used transfer learning techniques and experimented with transformer-based models pretrained on corpora collected from different resources, such as Bidirectional Encoder Representations from Transformers (BERT), Clinical BERT, BioBERT, and BlueBERT. We used a manually labeled data set that contained 150 question pairs in the pharmaceutical regulatory domain to evaluate the performance of our model. Results The Clinical BERT model performed better than other domain-specific BERT-based models in identifying question similarity from the RA domain. The BERT model had the best ability to learn domain-specific knowledge with transfer learning, which reached the best performance when fine-tuned with sufficient clinical domain question pairs. The top-performing model achieved an accuracy of 90.66% on the test set. Conclusions This study demonstrates the possibility of using pretrained language models to recognize question similarity in the pharmaceutical regulatory domain. Transformer-based models that are pretrained on clinical notes perform better than models pretrained on biomedical text in recognizing the question’s semantic similarity in this domain. We also discuss the challenges of using data augmentation techniques to address the lack of relevant data in this domain. The results of our experiment indicated that increasing the number of training samples using back translation and entity replacement did not enhance the model’s performance. This lack of improvement may be attributed to the intricate and specialized nature of texts in the regulatory domain. Our work provides the foundation for further studies that apply state-of-the-art linguistic models to regulatory documents in the pharmaceutical industry.

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“…Li et al (2021) analyzed agricultural Text-to-Speech (TTS) and discussed text analysis, rhythm generation, and speech synthesis of the TTS system. Wang et al (2021Wang et al ( , 2022 proposed a BERT based similarity matching model for agricultural questions, which rapidly and automatically detects questions that have the same semantic content, and proposed classification model based on the CNN structure to recognize questions about rice in particular. Jin et al (2020) proposed a classification model, also based on the CNN structure, to distinguish agricultural questions and their short text answers.…”

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confidence: 99%

Relation Extraction for Knowledge Graph Generation in the Agriculture Domain: A Case Study on Soybean Pests and Disease

Wang¹,

Zhang²,

Wang³

et al. 2023

Applied Engineering in Agriculture

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HighlightsWith the aim to reduce the burden of acquiring expert knowledge and strengthen the connection between written knowledge and the fields, this article investigated the problem of automatically extracting and organizing soybean pests and disease knowledge from text.Entities and relations were extracted using multiple models with deep neural network structures. Performance of these models were compared and evaluated in detail.A knowledge graph was automatically constructed using the extracted information, and made publicly available.ABSTRACT. Precision agriculture is an emerging type of agriculture that intensively uses information technology to automate agricultural production. Soybean (Glycine max (L.) Merri.), is an important crop in China, with an annual demand of approximately 110 million tons. However, in China, soybean production is threatened by more than 30 kinds of disease and 100 kinds of pests. With the rapidly increasing specialized information in the literature, it is difficult for farmers to keep up. Relation extraction automatically identifies and extracts structured knowledge from natural language text and thus can help to alleviate the problem. In this study, we propose to employ relation extraction to systematically extract information from expert-written text, and generate a knowledge graph from the extracted information. This case study was planned in China, therefore we mainly used Chinese texts. Firstly, we carefully chose expert-written text on soybean pests and disease, labeled the entities, and classified their thematic relations into five categories. Then, we built and trained three relation extraction models using state-of-the-art deep learning architectures and evaluated their performance on our task. Finally, we constructed an example knowledge graph from the extracted information and demonstrated their potential usage for automatic reasoning and solution recommendation for pests and disease prevention. In total, this study sampled 1038 entities and 1569 relation instances. Experimental results showed that our best model achieved an F1 score of 98.49% on identifying relations from text. Experimental results also showed the effectiveness of the example knowledge graph. Keywords: Bidirectional encoder representation from transformers, Knowledge graph, Relation extraction, Soybean pests and disease.

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A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Cited by 13 publications

References 23 publications

A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

Identifying the Question Similarity of Regulatory Documents in the Pharmaceutical Industry by Using the Recognizing Question Entailment System: Evaluation Study

Relation Extraction for Knowledge Graph Generation in the Agriculture Domain: A Case Study on Soybean Pests and Disease

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