A Combination of RNN and CNN for Attention-based Relation Classification

Zhang, Xiaobin; Chen, Fucai; Huang, Ruiyang

doi:10.1016/j.procs.2018.04.221

Cited by 65 publications

(24 citation statements)

References 4 publications

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“…They tested their model on the dataset and achieved 84.39%. Zhang et al [27] proposed a RCNN model that combines RNN and CNN in the network structure. This model got a F1-score 83.7%.…”

Section: Experiments Resultsmentioning

confidence: 99%

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Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

Jin

Guo

2020

Symmetry

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Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities in a sentence. In particular, we combine LSTM with attention mechanism to obtain the shallow local information and introduce a filter layer based on attention mechanism to strength the available information. Besides, we design a semantic rule for marking the key word between the target words and construct a key word layer to extract its semantic information. We evaluated the performance of our model on SemEval-2010 Task8 dataset and KBP-37 dataset. We achieved an F1-score of 86.3% on SemEval-2010 Task8 dataset and F1-score of 61.4% on KBP-37 dataset, which shows that our method is superior to the previous state-of-the-art methods.

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“…They tested their model on the dataset and achieved 84.39%. Zhang et al [27] proposed a RCNN model that combines RNN and CNN in the network structure. This model got a F1-score 83.7%.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Bidirectional LSTM has better performance than standard LSTM. Zhang et al [27] proposed an RCNN (Recurrent Convolutional Neural Networks) model, which combines the advantages of RNN and CNN. It not only solved the problem of long-time dependence with RNN, but also extracted more abundant features with a CNN.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

Jin

Guo

2020

Symmetry

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“…e proposed joint extraction model is applied to Chinese data sets to verify its validity. BRNN [28], SDP-BLSTM [29], CNN [30], Att-RCNN [31], and Hybrid Bi-LSTM-Siamese [32] are also carried out as the base line for the scalability evaluation.…”

Section: Experimental Designmentioning

confidence: 99%

A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Pan

et al. 2021

Complexity

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To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.

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“…Recently Deep Learning has strongly influenced semantic relation learning. Word embeddings can provide attributional features for a variety of learning frameworks (Attia et al, 2016;Vylomova et al, 2016), and the sentential context -in its entirety, or only the structured (through grammatical relations) or unstructured phrase expressing the relation -can be modeled through a variety of neural architectures -CNN (Tan et al, 2018;Ren et al, 2018) or RNN variations (Zhang et al, 2018). Speer et al (2008) introduce AnalogySpace, a representation of concepts and relations in CONCEPT-NET built by factorizing a matrix with concepts on one axis and their features or properties (according to CONCEPTNET) on the other.…”

Section: Semantic Relation Classificationmentioning

confidence: 99%

Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting

Becker¹,

Staniek²,

Năstase³

et al. 2019

RELATIONS - Workshop on Meaning Relations Between Phrases and Sentences

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Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.

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A Combination of RNN and CNN for Attention-based Relation Classification

Cited by 65 publications

References 4 publications

Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting

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