Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data

Luo, Xiong; Zhou, Wenwen; Wang, Weiping; Zhu, Yueqin; Deng, Jiankang

doi:10.1109/access.2017.2785229

Cited by 76 publications

(35 citation statements)

References 20 publications

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“…Li et al [22] used the GRU model to extract the Bacteria Biotope event from the biomedical literature on the BioNLP'16 corpus, and the results confirm the architecture's validity. Luo et al [23] used the GRU model to extract the geological data relations and achieved a satisfactory result. Shen et al [6] used the GRU framework to extract the relationship between disease and complications and the relationship between disease and symptoms in electronic health records.…”

Section: Related Workmentioning

confidence: 99%

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Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Zhang

2020

IEEE Access

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Relationship extraction among diseases, symptoms and tests has always been a concerning research issue in the biomedical field. Disease-pertinent relationship extraction for user-generated content in the online health community represents a research trend. By training the word embedding vectors for the medical-health field, conducting entity recognition and relationship annotation, and using deep learning technology, we construct a relation extraction model for extracting the relationships among diseases, symptoms and tests. Our relationship extraction model of the bidirectional gate recurrent unit (BiGRU) network based on character-level and sentence-level attention mechanisms achieved the best results on question-answer data in the online health community. Our research results can not only help physician diagnoses but also help patients perform health management, which has important industrial application value. INDEX TERMS Knowledge extraction, relationship extraction, attention mechanism, deep learning, bidirectional gated recurrent unit (BiGRU), online health community.

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Section: Related Workmentioning

confidence: 99%

“…We use the attention mechanism to find words that have a significant impact on the output, giving it a higher weight, so that its semantic information can be fully obtained. In our study, we used the character-level and sentence-level attention mechanism to extract the relationship among diseases, symptoms and tests [21], [23].…”

Section: Attention Mechanismmentioning

confidence: 99%

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Zhang

2020

IEEE Access

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“…CNN and RNN, two main representative DL models, have been successfully used for geological data mining and have achieved state-of-the-art results/performance on various geological data mining tasks. Luo et al (2017) applied DL approaches to analyze geological data. They proposed an attention-based mechanism for geological text relation extraction by incorporating a bidirectional gated recurrent unit and a highway network method to capture much more semantic information.…”

Section: For Geological Data Miningmentioning

confidence: 99%

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

Qiu

et al. 2020

Earth and Space Science

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Massive unstructured geoscience data are buried in geological reports. Geological text classification provides opportunities to leverage this wealth of data for geology and mineralization research. Existing studies of massive geoscience documents/reports have not provided effective classification results for further knowledge discovery and data mining and often lack adequate domain‐specific knowledge. In this paper, we present a novel and unified framework (namely, Dic‐Att‐BiLSTM) that combines domain‐specific knowledge and bidirectional long short‐term memory (BiLSTM) for effective geological text classification. Dic‐Att‐BiLSTM benefits from a matching strategy by incorporating domain‐specific knowledge developed based on geoscience ontology to grasp the linguistic geoscience clues. Furthermore, Dic‐Att‐BiLSTM brings together the capacity of a geoscience dictionary matching approach and an attention mechanism to construct a dictionary attention layer. Finally, the network framework of Dic‐Att‐BiLSTM can utilize domain‐specific knowledge and classify geological text automatically. Experimental verifications are conducted on two constructed data sets, and the results clearly indicate that Dic‐Att‐BiLSTM outperforms other state‐of‐the‐art text classification models.

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“…Other works apply several attention layers, such as word, relation and pooling attention , multi-head attention (Verga et al, 2018) and word-and entity-based attention (Jat et al, 2017). Luo et al (2018) 3 Richer-but-Smarter SDP…”

Section: Related Workmentioning

confidence: 99%

“…† : We failed to reproduce good result with the Multi-Att-CNN model, the performance of our implementation is just about 84.9. ‡ : Another re-implemented result of Multi-Att-CNN model reported by Luo et al (2018).…”

Section: Modelmentioning

confidence: 99%

A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

Can

et al. 2019

Proceedings of the 2019 Conference of the North

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To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but-Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https: //github.com/catcd/RbSP.

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Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data

Cited by 76 publications

References 20 publications

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Dictionary‐Based Automated Information Extraction From Geological Documents Using a Deep Learning Algorithm

A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

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