A pattern-aware self-attention network for distant supervised relation extraction

Shang, Yu-Ming; Huang, Heyan; Sun, Xin; Wei, Wei; Mao, Xian-Ling

doi:10.1016/j.ins.2021.10.047

Cited by 28 publications

(8 citation statements)

References 17 publications

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“…Therefore, the model can focus on all correctly labeled instances in the bag to improve the precision of relation extraction. Shan et al [19] proposed a relation extraction model based on pattern-aware self-attention, which can automatically identify various forms of relation patterns without losing global dependencies. Li et al [20] incorporated an entity-aware embedding module and a self-attention enhanced selective gate mechanism to integrate task-specific entity information into word embedding and then generated a complementary context-enriched representation for PCNN.…”

Section: Related Workmentioning

confidence: 99%

Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning

Zheng,

Xu,

Liu

et al. 2024

Neural Process Lett

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Relation extraction is an important task in information extraction, which aims to identify the relation between two given entities. The algorithm based on distant supervision can automatically generate a large amount of annotated data, which becomes the main method to deal with the task of relation extraction. However, previous studies rely too much on the precision of supervision information and ignore the effective supervision information hidden in the case of mislabeling, which leads to the loss of supervision information. To solve this problem, we propose the distantly supervised relation extraction model based on residual attention and self-learning. The model uses residual attention to extract features, and then uses self-learning idea to generate corrected labels for training data, which are added into the training process as supervisory signals to prevent parameter error updates caused by noisy labels. The model can not only reduce the problem of mislabeling caused by distant supervision, but also makes full use of the available supervisory information in the data to improve data utilization. Experiments show that compared with the existing mainstream baseline methods, the proposed model has higher precision and recall.

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Section: Related Workmentioning

confidence: 99%

Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning

Zheng,

Xu,

Liu

et al. 2024

Neural Process Lett

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“…( 15). (15) The and are the minimum and maximum dimensions, respectively, in the PH. The dimensions are 0 and 1, respectively, in this study.…”

Section: Computation Of Persistence Homology (Ph) In the Tda-ml Approachmentioning

confidence: 99%

“…In extracting information from data sets, there is a need for preprocessing (pre-training of datasets), relation prediction (i.e. similarity in the metric distances), datasets, and evaluation metrics [15,16]. The result is more robust when machine learning procedures are combined and integrated with a support vector machine (SVM) classifier [17].…”

Section: Graphical Abstract 1 Introductionmentioning

confidence: 99%

Application of topological data analysis to flood disaster management in Nigeria

Ohanuba

Ismail

Ali

2022

Environmental Engineering Research

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The importance of this research to the literature lies in the ability to develop a hybrid method of Topological Data Analysis and Unsupervised Machine Learning (TDA-uML) for flood detection. The working method in TDA-uML entails collection and loading of datasets, feature representation, data preprocessing (i.e., training, testing, computation, and validation), and data classification. Three properties make TDA distinct from traditional methods: coordinate-invariance, deformative-invariance, and compressed-representative. Formerly utilized hydrologic, hydraulic, and statistical models for flood control were frequently erroneous in their forecasts, lacked the use of hybrid models, and were not validated. The main research objective is to develop a hybrid method for predicting floods. The motivation is to fill research gaps by using TDA-uML methods for persistent homology (PH) and synthetic k-means clustering. The results will be used to compare and categorize the features that are produced. Seven states were selected based on Nigeria's flood history and affected population. The 7 states are located within the 8 hydrological areas of Nigeria. The efficiency of the resultant validity was 91%. The findings contributed to the development of a model for flood prediction and management; topological features were extracted from the data to predict and categorize the risk.

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“…Early studies [20][21][22][23] focused on predicting the relationship between two entities in a single sentence. However, an increasing number of relationship facts need to be extracted through multiple sentences, that is, to perform document-level relation extraction.…”

Section: Relation Extraction Relation Extraction Aims To Extract Rela...mentioning

confidence: 99%

A Novel Threat Intelligence Information Extraction System Combining Multiple Models

Guo

Chen

et al. 2022

Security and Communication Networks

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The increasing number of cyberattacks has made the cybersecurity situation more serious. Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing cyber environment. However, cyber threat intelligence usually exists in an unstructured form, and a huge amount of data poses a great challenge to security analysts. To this end, this paper proposes a novel threat intelligence information extraction system combining multiple models, which contains four key steps: entity extraction, coreference resolution, relation extraction, and knowledge graph construction. In the entity extraction task, a multihead self-attention mechanism is adopted to extract the dependency relationships between words. In the coreference resolution task, contextual information and mention embedding are fused to improve the mention representation. Meanwhile, features of different dimensions are extracted using a convolutional neural network. In the relation extraction task, additional features such as part of speech, mention width, entity type, and distance of entity pairs are incorporated to improve the embedding representation. Finally, a knowledge graph is constructed to explicitly present entities and their relationships. Experimental results indicate that compared with the baseline model, the F1 score of our model is improved by at least 8.87, 9.82, and 10.56 on entity extraction, coreference resolution, and relation extraction, respectively. The knowledge graph in Neo4j demonstrates the effectiveness of our system.

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A pattern-aware self-attention network for distant supervised relation extraction

Cited by 28 publications

References 17 publications

Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning

Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning

Application of topological data analysis to flood disaster management in Nigeria

A Novel Threat Intelligence Information Extraction System Combining Multiple Models

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