scholarly journals LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Neha Warikoo ◽  
Yung-Chun Chang ◽  
Wen-Lian Hsu
Keyword(s):  
Tree Kernel ◽  
Dependency Tree ◽  
Kernel Approach

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 Estimates of foreign exchange risk premia: a pricing kernel approach

2007 ◽  
Vol 35 (3) ◽  
pp. 475-495 ◽  
Author(s):  
Lorenzo Cappiello ◽  
Nikolaos Panigirtzoglou
Keyword(s):  
Foreign Exchange ◽  
Risk Premia ◽  
Pricing Kernel ◽  
Foreign Exchange Risk ◽  
Exchange Risk ◽  
Kernel Approach

Integrating Geophysics and Soil Sampling for Site Characterization: A Kernel Approach

2017 ◽  
Vol 22 (3) ◽  
pp. 305-308 ◽  
Author(s):  
Gabriel Fabien-Ouellet ◽  
Erwan Gloaguen ◽  
Gaël Plassart
Keyword(s):  
Site Characterization ◽  
Soil Sampling ◽  
Kernel Approach

scholarly journals A Kernel-Based Approach for Biomedical Named Entity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rakesh Patra ◽  
Sujan Kumar Saha
Keyword(s):  
Kernel Function ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Kernel Functions ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Named Entity ◽  
Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

Tree kernel-based semantic role labeling with enriched parse tree structure

2011 ◽  
Vol 47 (3) ◽  
pp. 349-362 ◽  
Author(s):  
GuoDong Zhou ◽  
Junhui Li ◽  
Jianxi Fan ◽  
Qiaoming Zhu
Keyword(s):  
Tree Structure ◽  
Parse Tree ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Tree Kernel

Foley–Sammon Optimal Discriminant Vectors Using Kernel Approach

2005 ◽  
Vol 16 (1) ◽  
pp. 1-9 ◽  
Author(s):  
W. Zheng ◽  
L. Zhao ◽  
C. Zou
Keyword(s):  
Kernel Approach
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