CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification

Shao, Cong; Li, Min; Li, Gang; Zhou, Mingle; Han, Delong

doi:10.3390/app122111068

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Compared with the BiLSTM-attention and multi-attention CNN based on bidirectional long-short-term memory network and attention mechanism, it increased by 3.7% and 2.7%, respectively. Compared with CRSAtt [37] and FA-RCNet [38] based on the BERT pre-trained model, it increased by 0.8% and 0.9%, respectively. The experimental results on the emEval-2010 Task 8 dataset showed that the relationship classification model proposed in this paper also achieved good results on the SemEval-2010 Task 8 dataset.…”

Section: Comparative Experiments Results and Analysismentioning

confidence: 98%

“Standard Text” Relational Classification Model Based on Concatenated Word Vector Attention and Feature Concatenation

et al. 2023

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The task of relation classification is an important pre-task in natural language processing tasks. Relation classification can provide a high-quality corpus for tasks such as machine translation, human–computer dialogue, and structured text generation. In the process of the digitalization of standards, identifying the entity relationship in the standard text is an important prerequisite for the formation of subsequent standard knowledge. Only by accurately labeling the relationship between entities can there be higher efficiency and accuracy in the subsequent formation of knowledge bases and knowledge maps. This study proposes a standard text relational classification model based on cascaded word vector attention and feature splicing. The model was compared and ablated on our labeled standard text Chinese dataset. At the same time, in order to prove the performance of the model, the above experiments were carried out on two general English datasets, SemEval-2010 Task 8 and KBP37. On standard text datasets and general datasets, the model proposed in this study achieved excellent results.

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Section: Comparative Experiments Results and Analysismentioning

confidence: 98%

“Standard Text” Relational Classification Model Based on Concatenated Word Vector Attention and Feature Concatenation

et al. 2023

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“…Using the enhanced data set for model training can obtain better model weights. CRSAtt [44] proposes a BERT-based relation classification model. CRSAtt processes BERT output features by fusing sentences and entity features.…”

Section: Methodsmentioning

confidence: 99%

“…In computer vision tasks, the visual attention mechanism [45,46] can make the model better notice the key information in the picture. In natural language processing tasks, the attention mechanism [44,47,48] can analyze the relationship between each word in the text and words of different parts of speech through the attention mechanism. The purpose of this is to obtain higher-quality semantic features.…”

Section: Methodsmentioning

confidence: 99%

FA-RCNet: A Fused Feature Attention Network for Relationship Classification

Tian

Zhou

et al. 2022

Applied Sciences

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Relation extraction is an important task in natural language processing. It plays an integral role in intelligent question-and-answer systems, semantic search, and knowledge graph work. For this task, previous studies have demonstrated the effectiveness of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in relational classification tasks. Recently, due to the superior performance of the pre-trained model BERT, BERT has become a feature extraction module for many relational classification models, and good results have been achieved in work related to BERT. However, most of such work uses the deepest levels of features. The important role of shallow-level information in the relational classification task is ignored. Based on the above problems, a relationship classification network FA-RCNet (fusion-attention relationship classification network) with feature fusion and attention mechanism is proposed in this paper. FA-RCNet fuses shallow-level features with deep-level features, and augments entity features and global features by the attention module so that the feature vector can perform the relational classification task more perfectly. In addition, the model in this paper achieves advanced results on both the SemEval-2010 Task 8 dataset and the KBP37 dataset compared to previously published models.

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Analysis of Digital Information in Storage Devices Using Supervised and Unsupervised Natural Language Processing Techniques

2023

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Due to the advancement of technology, cybercrime has increased considerably, making digital forensics essential for any organisation. One of the most critical challenges is to analyse and classify the information on devices, identifying the relevant and valuable data for a specific purpose. This phase of the forensic process is one of the most complex and time-consuming, and requires expert analysts to avoid overlooking data relevant to the investigation. Although tools exist today that can automate this process, they will depend on how tightly their parameters are tuned to the case study, and many lack support for complex scenarios where language barriers play an important role. Recent advances in machine learning allow the creation of new architectures to significantly increase the performance of information analysis and perform the intelligent search process automatically, reducing analysis time and identifying relationships between files based on initial parameters. In this paper, we present a bibliographic review of artificial intelligence algorithms that allow an exhaustive analysis of multimedia information contained in removable devices in a forensic process, using natural language processing and natural language understanding techniques for the automatic classification of documents in seized devices. Finally, some of the open challenges technology developers face when generating tools that use artificial intelligence techniques to analyse the information contained in documents on seized devices are reviewed.

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CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification

Cited by 3 publications

References 25 publications

“Standard Text” Relational Classification Model Based on Concatenated Word Vector Attention and Feature Concatenation

“Standard Text” Relational Classification Model Based on Concatenated Word Vector Attention and Feature Concatenation

FA-RCNet: A Fused Feature Attention Network for Relationship Classification

Analysis of Digital Information in Storage Devices Using Supervised and Unsupervised Natural Language Processing Techniques

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