Graph Convolution Network

method ID: graphconvolutionnetwork

Related Papers

Event Detection with Multi-Order Graph Convolution and Aggregated Attention

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractSyntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related …...

8934

Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractWe propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked …...

3803

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution Networks …...

30774

Constructing Flow Graphs from Procedural Cybersecurity Texts

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant informa- tion or identify the instruction-flow to com- plete a task, …...

33839

Label-Specific Dual Graph Neural Network for Multi-Label Text Classification

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractMulti-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for …...

8513

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractTemporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. …...

8389

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this …...

31350