Title :
Polyphonic Word Disambiguation with Machine Learning Approaches
Author :
Liu, Jinke ; Qu, Weiguang ; Tang, Xuri ; Zhang, Yizhe ; Sun, Yuxia
Author_Institution :
Sch. of Comput. Sci., Nanjing Normal Univ., Nanjing, China
Abstract :
Five different classification models, namely RFR_SUM, CRFs, Maximum Entropy, SVM and Semantic Similarity Model, are employed for polyphonic disambiguation. Based on observation of the experiment outcome of these models, an additional ensemble method based on majority voting is proposed. The ensemble method obtains an average precision of 96.78%, which is much better than the results obtained in previous literatures.
Keywords :
character recognition; entropy; learning (artificial intelligence); natural language processing; random processes; support vector machines; word processing; CRF; RFR_SUM; SVM; ensemble method; machine learning; majority voting; maximum entropy; polyphonic word disambiguation; relative frequency ratio; semantic similarity model; Context; Context modeling; Entropy; Kernel; Machine learning; Semantics; Support vector machines; CRFs; Ensemble model; Maximum Entropy; Polyphone disambiguation; RFR_SUM; SVM; Semantic Similarity;
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-8891-9
Electronic_ISBN :
978-0-7695-4281-2
DOI :
10.1109/ICGEC.2010.67
