From Treebank Conversion to Automatic Dependency Parsing for Vietnamese

2014 ◽  
pp. 196-207 ◽  
Author(s):  
Dat Quoc Nguyen ◽  
Dai Quoc Nguyen ◽  
Son Bao Pham ◽  
Phuong-Thai Nguyen ◽  
Minh Le Nguyen
Keyword(s):  
Dependency Parsing

Query expansion based on global dependency parsing analysis

2013 ◽  
Author(s):  
Qinyuan Xiang ◽  
Weijiang Li ◽  
Hui Deng ◽  
Feng Wang
Keyword(s):  
Query Expansion ◽  
Dependency Parsing

A Burmese Dependency Parsing Method Based on Transfer Learning

2020 ◽  
Author(s):  
Cunli Mao ◽  
Zhibo Man ◽  
Zhengtao Yu ◽  
Zhenhan Wang ◽  
Shengxiang Gao ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Dependency Parsing

Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing

2021 ◽  
Vol 20 (2) ◽  
pp. 1-12
Author(s):  
Shumin Shi ◽  
Dan Luo ◽  
Xing Wu ◽  
Congjun Long ◽  
Heyan Huang
Keyword(s):  
Language Processing ◽  
Manual Annotation ◽  
Syntactic Parsing ◽  
Dependency Parsing ◽  
Low Resource ◽  
Resource Setting ◽  
Dependency Tree ◽  
Low Resource Setting ◽  
Novel Method ◽  
Multi Level

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.

scholarly journals Dependency Parsing with Lattice Structures for Resource-Poor Languages

2009 ◽  
Vol E92-D (10) ◽  
pp. 2122-2136 ◽  
Author(s):  
Sutee SUDPRASERT ◽  
Asanee KAWTRAKUL ◽  
Christian BOITET ◽  
Vincent BERMENT
Keyword(s):  
Lattice Structures ◽  
Dependency Parsing ◽  
Resource Poor

Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

2021 ◽  
Vol 12 (5) ◽  
pp. 1-21
Author(s):  
Changsen Yuan ◽  
Heyan Huang ◽  
Chong Feng
Keyword(s):  
Cooperative Learning ◽  
State Of The Art ◽  
Relation Extraction ◽  
Sentence Length ◽  
Universal Relation ◽  
Dependency Parsing ◽  
Convolutional Network ◽  
Syntactic Features ◽  
Use Dependency

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

Sign in / Sign up

Export Citation Format

Share Document