Word prediction via a clustered optimal binary search tree
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 1, Issue 1 (31 Jan. 2004), pp.135-141, 7 p.
Publisher
Publication Date
2004-01-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Word prediction methodologies depend heavily on the statistical approach that uses the unigram, bigram, and the trigram of words.
However, the construction of the N-gram model requires a very large size of memory, which is beyond the capability of many existing computers.
Beside this, the approximation reduces the accuracy of word prediction.
In this paper, we suggest to use a cluster of computers to build an Optimal Binary Search Tree (OBST) that will be used for the statistical approach in word prediction.
The OBST will contain extra links so that the bigram and the trigram of the language will be presented.
In addition, we suggest the incorporation of other enhancements to achieve optimal performance of word prediction.
Our experimental results showed that the suggested approach improves the keystroke saving.
American Psychological Association (APA)
al-Qawasimah, Iyas. 2004. Word prediction via a clustered optimal binary search tree. The International Arab Journal of Information Technology،Vol. 1, no. 1, pp.135-141.
https://search.emarefa.net/detail/BIM-12472
Modern Language Association (MLA)
al-Qawasimah, Iyas. Word prediction via a clustered optimal binary search tree. The International Arab Journal of Information Technology Vol. 1, no. 1 (Jan. 2004), pp.135-141.
https://search.emarefa.net/detail/BIM-12472
American Medical Association (AMA)
al-Qawasimah, Iyas. Word prediction via a clustered optimal binary search tree. The International Arab Journal of Information Technology. 2004. Vol. 1, no. 1, pp.135-141.
https://search.emarefa.net/detail/BIM-12472
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 140
Record ID
BIM-12472