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Joint Event Extraction along Shortest Dependency Paths using Graph Convolutional Networks
[article]
2020
arXiv
pre-print
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Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent ...
path (SDP) in the dependency graph. ...
[29] used graph convolution networks (GCNs) to model syntactic information in the dependency graph. ...
arXiv:2003.08615v1
fatcat:hfv734fdwrgf7dslzlgdikmhyy
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event Extraction
[article]
2021
arXiv
pre-print
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This paper proposes an effective use of Graph Convolutional Networks(GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event ex-traction in commodity news. ...
However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-event relations, which can then be used for commodity ...
The proposed solution uses a Graph Convolutional Network with a contextual sub-tree to extract events effectively. ...
arXiv:2109.12781v1
fatcat:7ubgfb3znjek3puqtadecejdn4
Planning and Learning: A Review of Methods involving Path-Planning for Autonomous Vehicles
[article]
2022
arXiv
pre-print
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We give a brief introduction of neural networks. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs. ...
Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty. ...
This process is repeated for each layer. propose Spatio-Temporal Graph Convolutional Networks (STGCN), which uses a 1D-CNN alongside the ChebNet convolutional layer. ...
arXiv:2207.13181v1
fatcat:o7o4ss2l2vbfpcb4kal34u3mj4
Multi-Scale Feature and Metric Learning for Relation Extraction
[article]
2021
arXiv
pre-print
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Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricted receptive field. ...
We also design a multi-scale graph convolutional network which can increase the receptive field towards specific syntactic roles. ...
Meanwhile, the dependency based GCN methods can only convolute along the syntactic paths. ...
arXiv:2107.13425v1
fatcat:fav425fzzrdy3dujlntcznh6he
Matching Article Pairs with Graphical Decomposition and Convolutions
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1802.07459v1 fatcat:w4m6dl7uejccfbnmt5niryjqvi
network. ...
We then match a pair of articles by comparing the sentences that enclose the same concept vertex through a series of encoding techniques, and aggregate the matching signals through a graph convolutional ...
and concepts along the paths [28] . ...
arXiv:1802.07459v2
fatcat:x436jyfbxjeb7euf5fmbp5nxsa
Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks
2022
Information
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Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. ...
The attention layer is combined with a dependency tree to enhance the model's ability to perceive relational features, and a bi-directional graph convolutional network is constructed to further capture ...
SDP-LSTM [27] : a multichannel recurrent neural network with long short-term memory (LSTM) units is used to pick up heterogeneous information along the SDP (shortest dependency path between two entities ...
doi:10.3390/info13080364
fatcat:ehadgpncf5htnlz5r5hutj5lui
Feedback Graph Convolutional Network for Skeleton-based Action Recognition
[article]
2020
arXiv
pre-print
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Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. ...
In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces the feedback mechanism into GCNs and action recognition. ...
For a vertex v ti in the graph, its neighbor set is denoted as N (v ti ) = {v tj |d(v ti , v tj ) ≤ D}, where d(v ti , v tj ) is the length of the shortest path from v tj to v ti . ...
arXiv:2003.07564v1
fatcat:mp7qkpj6hzbnzprdtrnvl2khj4
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction
[article]
2021
arXiv
pre-print
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Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models ...
We introduce GATE, a Graph Attention Transformer Encoder, and test its cross-lingual transferability on relation and event extraction tasks. ...
All rights reserved. 1 The shortest path in the dependency graph structure. ...
arXiv:2010.03009v2
fatcat:h6xomdspjzh6xhfmvsmii7n2mu
A Comprehensive Survey on Graph Neural Networks
[article]
2019
arXiv
pre-print
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We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and ...
spatial-temporal graph neural networks. ...
PGC-DGCNN [46] increases the contributions of distant neighbors based on shortest paths. It defines a shortest path adjacency matrix S (j) . ...
arXiv:1901.00596v4
fatcat:xxuchvawonhczay2sgjgzw5wgu
Neural relation extraction: a survey
[article]
2020
arXiv
pre-print
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In this study, we present a comprehensive review of methods on neural network based relation extraction. ...
Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. ...
[66] takes advantage of the shortest dependency path (SDP) between entities. They claim that the words along SDP are more informative. ...
arXiv:2007.04247v1
fatcat:xxrcy2ef75dk5aeijqlf6tjgke
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
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It uses a generic specification language for the task and for data annotations in terms of spans and frames. ...
To address these limitations, we present DERE, a novel framework for declarative specification and compilation of templatebased information extraction. ...
Tokenization and stemming is done with NLTK (Loper and Bird, 2002) , dependency features are extracted with spacy (Honnibal and Johnson, 2015) and dependency graphs are stored and processed using NetworkX ...
doi:10.18653/v1/d18-2008
dblp:conf/emnlp/AdelBPPK18
fatcat:osp5ugahuvbkvceafvrd6ryd7e
An Indexing Framework for Queries on Probabilistic Graphs
2017
ACM Transactions on Database Systems
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In particular, we examine "source-to-target" queries (or ST-queries), such as computing the shortest path between two vertices. e major di erence with the deterministic se ing is that query answers are ...
Information in many applications, such as mobile wireless systems, social networks, and road networks, is captured by graphs. In many cases, such information is uncertain. ...
Moreover, we are interested in evaluating paths over a graph, not the joint distributions in its nodes. ...
doi:10.1145/3044713
fatcat:4ekoy7ahwbamhojmvse62yfafy
Cell Tracking via Proposal Generation and Selection
[article]
2017
arXiv
pre-print
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in a graphical model using edges representing different cellular events and poses joint cell detection and tracking as the selection of a subset of cell and edge proposals. ...
To efficiently and reliably extract useful insights from these captured sequences, automated cell tracking is essential. ...
BLOB generates cell proposals by using multiple elliptical filter banks, and performs cell tracking by iteratively finding the shortest path in the tracking graph. ...
arXiv:1705.03386v1
fatcat:tbuoc6ve7zfwjkiz2jvhjsgtfi
Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
[article]
2019
arXiv
pre-print
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Our deep learning approach entails composing various intermediate representations including sequence and graph based context, where the latter is derived using graph convolutions (GC) with a novel attention-based ...
As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination ...
[29] , wherein for a pair of subject and object entities (corresponding to t Sub and t Obj ), we keep only nodes either along or within one hop of the shortest dependency path. ...
arXiv:1910.12419v2
fatcat:nmb5j35aczcldg2h4ywoijiojy
Neural Metric Learning for Fast End-to-End Relation Extraction
[article]
2019
arXiv
pre-print
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We introduce a novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, that improves on the state-of-the-art ...
Relation extraction (RE) is an indispensable information extraction task in several disciplines. ...
Liu et al. (2016) proposed a dependency-based convolutional neural network (CNN) architecture wherein the convolution is applied over words adjacent according to the shortest path connecting the entities ...
arXiv:1905.07458v4
fatcat:ozzii4u4lrasxfojqeesasxktu
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