New indices for text: PAT Trees and PAT arrays
Information retrieval
Class-based n-gram models of natural language
Computational Linguistics
Proceedings of the 2nd international conference on Intelligent user interfaces
Parallel Construction of Multidimensional Binary Search Trees
IEEE Transactions on Parallel and Distributed Systems
A classification approach to word prediction
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Hi-index | 0.90 |
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.