A learning-classification based appro word prediction
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 4, Issue 3 (31 Jul. 2007), pp.264-271, 8 p.
Publisher
Publication Date
2007-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Word prediction is an important NLP problem in which we want to predict the correct word in a given Word completion utilities, predictive text entry systems, writing aids, and language translation are some of common word prediction applications.
This paper presents a new word prediction approach based on context features and machine learning.
The proposed method casts the problem as a learning-classification task by training word predictors with highly discriminating features selected by various feature selection techniques.
The contribution of this work lies in the new way of presenting this problem, and the unique combination of a top performer in machine learning, svm, with various feature selection techniques MI, X2, and more.
The method is implemented and evaluated using several datasets.
The experimental results show clearly that the method is effective in predicting the correct words by utilizing small contexts.
The system achieved impressive results, compared with similar work; the accuracy in some experiments approaches predictions.
American Psychological Association (APA)
al-Mubaid, Hisham. 2007. A learning-classification based appro word prediction. The International Arab Journal of Information Technology،Vol. 4, no. 3, pp.264-271.
https://search.emarefa.net/detail/BIM-11694
Modern Language Association (MLA)
al-Mubaid, Hisham. A learning-classification based appro word prediction. The International Arab Journal of Information Technology Vol. 4, no. 3 (Jul. 2007), pp.264-271.
https://search.emarefa.net/detail/BIM-11694
American Medical Association (AMA)
al-Mubaid, Hisham. A learning-classification based appro word prediction. The International Arab Journal of Information Technology. 2007. Vol. 4, no. 3, pp.264-271.
https://search.emarefa.net/detail/BIM-11694
Data Type
Journal Articles
Language
English
Notes
includes bibliographical references : p. 270-271
Record ID
BIM-11694