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Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel and effective Semantic Graph Convolutional Network (SGCN) to enhance the modeling of inter-argument semantics on a deeper interaction level for implicit discourse relation ...
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations ...
Conclusion We propose a novel and effective Semantic Graph Convolutional Network (SGCN) for implicit discourse relation classification, which can model the inter-arguments semantics structurally and capture ...
arXiv:1910.09183v1
fatcat:lqnyuwfcovaqrpysmd4gieelvm
Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition
2021
EAI Endorsed Transactions on Scalable Information Systems
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A classification model based on self-organizing incremental and graph convolutional neural network is constructed to obtain the argument representation which is helpful for English implicit discourse relation ...
To solve this problem, this paper proposes a self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition. ...
Conclusions In this paper, a graph convolutional neural network model based on self-organizing increment and interactive attention mechanism is proposed to recognize implicit discourse relations. ...
doi:10.4108/eai.22-11-2021.172215
fatcat:dx4swsvbtnbw3bww3cxz6ohs7q
A Survey of Implicit Discourse Relation Recognition
[article]
2022
arXiv
pre-print
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The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. ...
Finally, we discuss future research directions for discourse relation analysis. ...
A graph convolutional network is used to extract interactive features from the semantic interaction graph. ...
arXiv:2203.02982v1
fatcat:ubublxw2fnfdpexgw4jslj76tm
Context-specific Heterogeneous Graph Convolutional Network for Implicit Sentiment Analysis
2020
IEEE Access
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INDEX TERMS Implicit sentiment, sentiment analysis, heterogeneous graph, graph convolutional network (GCN). VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. ...
Graph convolutional network (GCN) techniques have been widely applied for sentiment analysis since they are capable of learning from complex structures and preserving global information. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable remarks and comments. ...
doi:10.1109/access.2020.2975244
fatcat:eztswbwtvbaatajonbmcfajppy
Towards Causal Explanation Detection with Pyramid Salient-Aware Network
[article]
2020
arXiv
pre-print
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PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. ...
Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. ...
The graph convolutional network (GCN) (Kipf and Welling, 2016) is a generalization of convolutional neural network (LeCun et al., 1998) for encoding graphs. ...
arXiv:2009.10288v2
fatcat:haerjf5ksff67m4b3o3bb3yaw4
Measuring Semantic Coherence of a Conversation
[article]
2018
arXiv
pre-print
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We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of ...
We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during ...
Convolutional neural network. ...
arXiv:1806.06411v1
fatcat:5htffhqbtndvxgpwq7jdx6bzu4
Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations
[article]
2019
arXiv
pre-print
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graph neural networks. ...
This article reports on two experiments that evaluate how effectively crowd-sourced and expert-annotated graphs can represent the multimodal structure of diagrams for representation learning using various ...
Graph Neural Networks I evaluated the following graph neural network architectures for both graph and node classification tasks: • Graph Convolutional Network (GCN) (Kipf and Welling, 2017) • Simplifying ...
arXiv:1912.02866v1
fatcat:u4snprup2jhkvazmoew5rhibf4
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. ...
Current discourse relation classifiers fall short in this respect. ...
We'd like to thank all the reviewers for their insightful and valuable comments. ...
doi:10.18653/v1/d19-1586
dblp:conf/emnlp/ShiD19
fatcat:rvqmcvdijfdfxondi2ctst5yhq
Variational Neural Discourse Relation Recognizer
[article]
2016
arXiv
pre-print
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Implicit discourse relation recognition is a crucial component for automatic discourselevel analysis and nature language understanding. ...
VarNDRR establishes a directed probabilistic model with a latent continuous variable that generates both a discourse and the relation between the two arguments of the discourse. ...
We also thank the anonymous reviewers for their insightful comments. ...
arXiv:1603.03876v2
fatcat:klpb4xt37rfhdpxugqihixe444
Variational Neural Discourse Relation Recognizer
2016
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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Generally, discourse relations can be divided into two categories: explicit and implicit, which can be illustrated in the following example: The company was disappointed by the ruling. because The obligation ...
We also thank the anonymous reviewers for their insightful comments. ...
. • SCNN: a shallow convolutional neural network proposed by Zhang et al. (2015) . ...
doi:10.18653/v1/d16-1037
dblp:conf/emnlp/ZhangXSLJDZ16
fatcat:rdm6xgbl6zg5zbpg4wapyrx57i
Learning Procedures from Text
2018
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18
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Recently, extracting semantic relations from the procedural text has been actively explored. ...
To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. ...
The task of implicit discourse relationship classification is to recognize how two adjacent sentences are associated without explicit discourse markers (e.g. "because"). ...
doi:10.1145/3184558.3186347
dblp:conf/www/ParkM18
fatcat:eq4s4pb4lvanho6hq57axwy4ye
Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
2021
Applied Sciences
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Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making ...
While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. ...
SemEvel2007 Benchmark dataset to 7. [49] task-4 is applied for finding 7 frequently occurring semantic Events pair patterns of 7 relation types. let the evaluation of diverse semantic relation classification ...
doi:10.3390/app112110064
fatcat:btv66da5x5a73auogv5d3lp2bi
Dialogue Graph Modeling for Conversational Machine Reading
[article]
2021
arXiv
pre-print
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Specifically, Discourse Graph is designed to learn explicitly and extract the discourse relation among rule texts as well as the extra knowledge of scenario; Decoupling Graph is used for understanding ...
And finally a global graph for fusing the information together and reply to the user with our final decision being either "Yes/No/Irrelevant" or to ask a follow-up question to clarify. ...
Acknowledgments We would like to thank all the anonymous reviewers for their helpful comments and suggestions. Also thanks to Max Bartolo for evaluating our submitted models on the hidden test set. ...
arXiv:2012.14827v3
fatcat:55x2wnxkjvhnpa47czlqxcbx24
Genetic Optimization in Hybrid Level Sentiment Analysis for Opinion Classification
2020
International Journal of Advanced Trends in Computer Science and Engineering
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To overcome this problem and to train the machine to perfection, we introduced a feature optimization algorithm with CNN (Convolutional Neural Network) as a classification algorithm. ...
Yet many approaches and models were developed for this problem, classification problem still remained. ...
has convolutional and recurrent neural networks. ...
doi:10.30534/ijatcse/2020/81922020
fatcat:nchkxhj7znannj62nnhpxu22x4
Table of Contents
2021
IEEE/ACM Transactions on Audio Speech and Language Processing
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Zhu Learning Context-Aware Convolutional Filters for Implicit Discourse Relation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Guanason Detection and Classification of Acoustic Scenes and Events Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks . . . . . . . ...
doi:10.1109/taslp.2021.3137066
fatcat:ocit27xwlbagtjdyc652yws4xa
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