An empirical convolutional neural network approach for semantic relation classification

Qin, Pengda; Wang, Xu; Guo, Jun

doi:10.1016/j.neucom.2015.12.091

Cited by 83 publications

(39 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to further enhance the accuracy of domain adaption, we further use the convolution neural network (CNN) [31] to extract the high-dimensional feature of images and complete the domain adaptation to improve the performance of our algorithm. The experimental results show that the SA-ELM-DA performs better than PCA and other state-of-the-art domain adaption algorithms on average accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, construct surf feature point description operator and get an 800-dimensional representation. Convolution neural network (CNN) [31] is an algorithm commonly used in depth learning; we use imagenet-vgg-f model of CNN in MatConvNet (a convolution network toolkit in MATLAB developed by Andrea Vedaldi) to extract the output of the fully connected layer on layer 20 and get a 4096-dimensional representation of images.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Domain Adaption Based on ELM Autoencoder

Zhang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance. The widely experimental results on Office/Caltech-256 datasets show that the proposed algorithm can achieve better classification accuracy than PCA subspace alignment algorithm and other state-of-the-art domain adaption algorithms in most cases.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Domain Adaption Based on ELM Autoencoder

Zhang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Semantic relation classification is the task of classifying the underlying abstract semantic relations between target entities (terms) present in texts [10]. The goal of relation classification is defined as follows: given a sentence S with the pairs of annotated target nominals e 1 and e 2 , the relation classification system aims to classify the relations between e 1 and e 2 in given texts within the pre-defined relation set [5].…”

Section: Semantic Relation Classificationmentioning

confidence: 99%

“…Recently, Neural network-based approaches have achieved significant improvement over traditional methods based on either human-designed features [10]. However, existing neural networks for relation classification are usually based on shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks).…”

Section: Existing Approaches For Semantic Relation Classificationmentioning

confidence: 99%

Composite Semantic Relation Classification

Barzegar

Freitas

Handschuh

et al. 2017

Natural Language Processing and Information Systems

View full text Add to dashboard Cite

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem.

show abstract

“…The prior knowledge merely comes from a unique randomly initialized vector, while our model adequately integrates entity pair information to adaptively calculate attention weights. It is noteworthy that Att-BLSTM utilizes Position Indicator [34] to annotate entity pair rather than position feature. In order to compare under the same condition, we reproduce its idea with position feature [11].…”

Section: Overall Performancementioning

confidence: 99%

Providing Definitive Learning Direction for Relation Classification System

Qin

Wang

Guo

2017

Journal of Control Science and Engineering

Self Cite

View full text Add to dashboard Cite

Deep neural network has adequately revealed its superiority of solving various tasks in Natural Language Processing, especially for relation classification. However, unlike traditional feature-engineering methods that targetedly extract well-designed features for specific task, the diversity of input format for deep learning is limited; word sequence as input is the frequently used setting. Therefore, the input of neural network, to some extent, lacks pertinence. For relation classification task, it is not uncommon that, without specific entity pair, a sentence contains various relation types; therefore, entity pair indicates the distribution of the crucial information in input sentence for recognizing specific relation. Aiming at this characteristic, in this paper, several strategies are proposed to integrate entity pair information into the application of deep learning in relation classification task, in a way to provide definitive learning direction for neural network. Experimental results on the SemEval-2010 Task 8 dataset show that our method outperforms most of the state-of-the-art models, without external linguistic features.

show abstract

An empirical convolutional neural network approach for semantic relation classification

Cited by 83 publications

References 23 publications

Domain Adaption Based on ELM Autoencoder

Domain Adaption Based on ELM Autoencoder

Composite Semantic Relation Classification

Providing Definitive Learning Direction for Relation Classification System

Contact Info

Product

Resources

About