LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese

Abstract

In Vietnamese dependency parsing, several methods have been proposed. Dependency parser which uses deep neural network model has been reported that achieved state-of-the-art results. In this paper, we proposed a new method which applies LSTM easy-first dependency parsing with pre-trained word embeddings and character-level word embeddings. Our method achieves an accuracy of 80.91% of unlabeled attachment score and 72.98% of labeled attachment score on the Vietnamese Dependency Treebank (VnDT).

References in corpus (6)

• Distributed Representations of Words and Phrases and their Compositionality
• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Transition-Based Dependency Parsing with Stack Long Short-Term Memory
• Enriching Word Vectors with Subword Information
• Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

Cited by in corpus (3)

• Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension
• A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing
• An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese