Weighted graph convolution over dependency trees for nontaxonomic relation extraction on public opinion information

2021 ◽  
Author(s):  
Guangyao Wang ◽  
Shengquan Liu ◽  
Fuyuan Wei
Keyword(s):  
Public Opinion ◽  
Relation Extraction ◽  
Weighted Graph ◽  
Dependency Trees

scholarly journals Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control

2019 ◽  
Author(s):  
Amir Pouran Ben Veyseh ◽  
Thien Nguyen ◽  
Dejing Dou
Keyword(s):  
Deep Learning ◽  
Information Flow ◽  
Language Processing ◽  
Relation Extraction ◽  
Learning Models ◽  
Information Flow Control ◽  
Cross Domain ◽  
Benchmark Datasets ◽  
Dependency Trees ◽  
Use Dependency

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees. One potential problem with this approach is it might prevent the models from capturing important context information beyond syntactic structures and cause the poor cross-domain generalization. This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. We also propose a new mechanism to control the information flow in the model based on the input entity mentions. Our extensive experiments on benchmark datasets show that the proposed model outperforms the existing methods for RE significantly.

scholarly journals Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction

2020 ◽  
Vol 34 (05) ◽  
pp. 9106-9113
Author(s):  
Amir Veyseh ◽  
Franck Dernoncourt ◽  
My Thai ◽  
Dejing Dou ◽  
Thien Nguyen
Keyword(s):  
Structure Prediction ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Relations ◽  
General Strategy ◽  
Novel Method ◽  
Dependency Trees ◽  
The Common ◽  
The One

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets.

scholarly journals Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph

2020 ◽  
Vol 34 (05) ◽  
pp. 8928-8935
Author(s):  
Kai Sun ◽  
Richong Zhang ◽  
Yongyi Mao ◽  
Samuel Mensah ◽  
Xudong Liu
Keyword(s):  
Relation Extraction ◽  
Weighted Graph ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Classification Task ◽  
Convolutional Network ◽  
Structure Information ◽  
Convolutional Networks ◽  
Dependency Tree ◽  
Relation Classification

A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Guided Graph Convolutional Networks (AGGCNs) transforms the dependency tree into a weighted-graph to distinguish the relevance of nodes and edges for relation classification. However, in their approach, the graph is fully connected, which destroys the structure information of the original dependency tree. How to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenge in the relation classification task. In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. We refer to this graph as a syntax-transport graph. We further propose a learnable syntax-transport attention graph convolutional network (LST-AGCN) which operates on the syntax-transport graph directly to distill the final representation which is sufficient for classification. Experiments on Semeval-2010 Task 8 and Tacred show our approach outperforms previous methods.

scholarly journals Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction

2020 ◽  
Author(s):  
Bowen Yu ◽  
Xue Mengge ◽  
Zhenyu Zhang ◽  
Tingwen Liu ◽  
Wang Yubin ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Dependency Trees

scholarly journals Semantic Enhanced Distantly Supervised Relation Extraction via Graph Attention Network

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 528
Author(s):  
Xiaoye Ouyang ◽  
Shudong Chen ◽  
Rong Wang
Keyword(s):  
New York ◽  
New York Times ◽  
Relation Extraction ◽  
Extraction Methods ◽  
Semantic Features ◽  
Attention Networks ◽  
Word Position ◽  
Supervised Methods ◽  
Dependency Trees ◽  
Type Information

Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. However, the automatically constructed datasets comprise amounts of low-quality sentences and noisy words, and the current Distantly Supervised methods ignore these noisy data, resulting in unacceptable accuracy. To mitigate this problem, we present a novel Distantly Supervised approach SEGRE (Semantic Enhanced Graph attention networks Relation Extraction) for improved relation extraction. Our model first uses word position and entity type information to provide abundant local features and background knowledge. Then it builds the dependency trees to remove noisy words that are irrelevant to relations and employs Graph Attention Networks (GATs) to encode syntactic information, which also captures the important semantic features of relational words in each instance. Furthermore, to make our model more robust against noisy words, the intra-bag attention module is used to weight the bag representation and mitigate noise in the bag. Through extensive experiments on Riedel New York Times (NYT) and Google IISc Distantly Supervised (GIDS) datasets, we demonstrate SEGRE’s effectiveness.

scholarly journals Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction

2021 ◽  
Vol 11 (4) ◽  
pp. 1480
Author(s):  
Haiyang Zhang ◽  
Guanqun Zhang ◽  
Ricardo Ma
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Local Context ◽  
Semantic Features ◽  
Global Context ◽  
Contextual Features ◽  
Current State ◽  
Syntactic Features ◽  
Dependency Trees ◽  
Depth Learning

Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.

Review of Reader in Public Opinion and communication.

1966 ◽  
Vol 11 (6) ◽  
pp. 316-316
Author(s):  
No authorship indicated
Keyword(s):  
Public Opinion
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