Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction
Source abstract
Abstract
Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sent... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Advanced Text Analysis Techniques · Natural Language Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (50)
Show source-provided references
1. Aspect-level sentiment analysis via convolution over dependency tree
2. Improving sentiment analysis in election-based conversations on twitter with elecbert language model
3. Question answering on Freebase via relation extraction and textual evidence
4. Focused meeting summarization via unsupervised relation extraction
5. Relation classification via convolutional deep neural network
6. D. Zhang and D. Wang, “Relation classification via recurrent neural network,” arXiv preprint arXiv: 1508.01006, 2015.
7. Classifying relations via long short term memory networks along shortest dependency paths
8. Classifying relations by ranking with convolutional neural networks
9. Bidirectional long short-term memory networks for relation classification
10. Relation classification via multi-level attention CNNs
11. Attention-based bidirectional long short-term memory networks for relation classification
12. Position-aware attention and supervised data improve slot filling
13. An improved baseline for sentence-level relation extraction
14. End-to-end relation extraction using LSTMs on sequences and tree structures
15. Graph convolution over pruned dependency trees improves relation extraction
16. Relation extraction with convolutional network over learnable syntax-transport graph
17. Relation extraction with type-aware map memories of word dependencies
18. Attention guided graph convolutional networks for relation extraction
19. Graph convolution over multiple dependency sub-graphs for relation extraction
20. Dependency-driven relation extraction with attentive graph convolutional networks
21. Learning to prune dependency trees with rethinking for neural relation extraction
22. A multi-feature fusion model for Chinese relation extraction with entity sense10.1016/j.knosys.2020.106348 ↗
23. A hybrid attention mechanism for multi-target entity relation extraction using graph neural networks
24. A hybrid attention mechanism for multi-target entity relation extraction using graph neural networks
25. A contextual dependency-aware graph convolutional network for extracting entity relations10.1016/j.eswa.2023.122366 ↗
26. Relation extraction for manufacturing knowledge graphs based on feature fusion of attention mechanism and graph convolution network10.1016/j.knosys.2022.109703 ↗
27. Document-level relation extraction with multi-layer heterogeneous graph attention network10.1016/j.engappai.2023.106212 ↗
28. Dual attention graph convolutional network for relation extraction
29. Towards deep understanding of graph convolutional networks for relation extraction10.1016/j.datak.2023.102265 ↗
30. Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction
31. Exploring various knowledge in relation extraction
32. Kernel methods for relation extraction
33. Aone · Apr. 29–May 1, 1998
34. SpanBERT: Improving pre-training by representing and predicting spans10.1162/tacl_a_00300 ↗
35. A knowledge-enriched and span-based network for joint entity and relation extraction
36. Distant supervised relation extraction with cost-sensitive loss
37. Relation extraction for massive news texts
38. Improving relation extraction through syntax-induced pre-training with dependency masking
39. Z. Wan et al., “GPT-RE: In-context learning for relation extraction using large language models,” arXiv preprint arXiv:2305.02105, 2023.10.18653/v1/2023.emnlp-main.214 ↗
40. How to unleash the power of large language models for few-shot relation extraction?
41. Y. Ozyurt, S. Feuerriegel, and C. Zhang, “In-context few-shot relation extraction via pre-trained language models,” arXiv preprint arXiv:2310.11085, 2023.
42. C. Peng et al., “Model tuning or prompt tuning? A study of large language models for clinical concept and relation extraction,” arXiv preprint arXiv:2310.06239, 2023.10.1016/j.jbi.2024.104630 ↗
43. J. Yang et al., “Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond,” arXiv preprint arXiv:2304.13712, 2023.
44. S. Longpre et al., “The flan collection: Designing data and methods for effective instruction tuning,” arXiv preprint arXiv:2301.13688, 2023.
45. BERT: Pre-training of deep bidirectional transformers for language understanding
46. SemEval-2010 Task 8: Multi-way classification of semantic relations between pairs of nominals
47. TACRED revisited: A thorough evaluation of the TACRED relation extraction task
48. Re-TACRED: Addressing shortcomings of the TACRED dataset10.1609/aaai.v35i15.17631 ↗
49. Matching the blanks: Distributional similarity for relation learning
50. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.