TY  - CONF
ID  - SisLab112
UR  - http://10.1109/rivf.2012.6169827
A1  - Dinh, Phu Hung
A1  - Nguyen, Ngoc Khuong
A1  - Le, Anh Cuong
Y1  - 2012/03/01/
N2  - Word Sense Disambiguation (WSD) is the task of determining the right sense of a word depending on the context it appears. Among various approaches developed for this task, statistical machine learning methods have been showing their advantages in comparison with others. However, there are some cases which cannot be solved by a general statistical model. This paper proposes a novel framework, in which we use the rules generated by transformation based learning (TBL) to improve the performance of a statistical machine learning model. This framework can be considered as a combination of a rule-based method and statistical based method. We have developed this method for the problem of Vietnamese WSD and achieved some promising results.
KW  - Accuracy;Context;Data models;Learning systems;Machine learning;Niobium;Training;learning (artificial intelligence);natural language processing;statistical analysis;Vietnamese word sense disambiguation;general statistical model;statistical machine learning model;transformation based learning;transformation rule learning;
TI  - Combining Statistical Machine Learning with Transformation Rule Learning for Vietnamese Word Sense Disambiguation
SP  - 1 
M2  - Ho Chi Minh city
AV  - none
EP  - 6
T2  - 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF)
ER  -