Enhancing dependency parsing with graph neural network-learned embeddings from universal dependency treebanks
Authors:
- The Quyen NgoTNThe Quyen Ngo
- Thi Minh Huyen NguyenTNThi Minh Huyen Nguyen
- Phuong Le-HongPLPhuong Le-Hong
- 15 May 2026
- journal article
- Published by Elsevier in Computer Speech & Language
- Vol. 101
Abstract
No abstract availableThis publication has 23 references indexed in Scilit:
- A Semantics-aware Head-driven Approach for Multilingual Dependency ParsingIEEE Access, 2025
- Contrasting Linguistic Patterns in Human and LLM-Generated News TextArtificial Intelligence Review, 2024
- Dissociating language and thought in large language modelsTrends in Cognitive Sciences, 2024
- Integrating graph embedding and neural models for improving transition-based dependency parsingNeural Computing and Applications, 2023
- How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVENTransactions of the Association for Computational Linguistics, 2023
- Named Entity Recognition as Dependency Parsing2020
- Efficient Second-Order TreeCRF for Neural Dependency Parsing2020
- Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature RepresentationsTransactions of the Association for Computational Linguistics, 2016
- node2vec2016
- Laplacian centrality: A new centrality measure for weighted networksInformation Sciences, 2012