Neural Relation Classification Using Selective Attention and Symmetrical Directional Instances

Tan, Zhen; Li, Bo; Huang, Peixin; Ge, Bin; Xiao, Weidong

doi:10.3390/sym10090357

Cited by 4 publications

(4 citation statements)

References 16 publications

(29 reference statements)

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“…Recently, many researchers have begun to apply deep learning techniques to RE [22, 23]. Socher et al [6] proposed using RNNs to solve RE problems; the method first parses a sentence and then learns the vector representation for each node on the syntax tree.…”

Section: Related Workmentioning

confidence: 99%

Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

Huang

Chen

et al. 2019

Computational Intelligence and Neuroscience

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Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.

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Section: Related Workmentioning

confidence: 99%

Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

Huang

Chen

et al. 2019

Computational Intelligence and Neuroscience

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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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“…They find out the optimal window size of 2 for training by applying different window size to the input sentence. For another work with the CNN model, they use two kinds of embeddings, position embedding and word embedding [10]. After embedding, the model generates a shortest dependency path from each sentence.…”

Section: Relation Classificationmentioning

confidence: 99%

FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network

2019

KSII TIIS

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As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

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Neural Relation Classification Using Selective Attention and Symmetrical Directional Instances

Cited by 4 publications

References 16 publications

Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

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

FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network

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