Authors:
Derwin Suhartono
;
Garry Wong
;
Polim Kusuma
and
Silviana Saputra
Affiliation:
Bina Nusantara University, Indonesia
Keyword(s):
Predictive Text, Word Prediction, n-gram, Prediction, KSPC, Keystrokes Saving.
Related
Ontology
Subjects/Areas/Topics:
Agent Communication Languages
;
Agents
;
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
Predictive text system is an alternative way to improve human communication, especially in matter of typing. Originally, predictive text system was intended for people who have flaws in verbal and motor. This system is aimed to all people who demands speed and accuracy in typing a document. There were many similar researches which develop this system that had their own strengths and weaknesses. This research attempts to develop the algorithm for predictive text system by combining four methods from previous researches and focus only in Bahasa (Indonesian language). The four methods consist of frequency, n-gram, probability table, and syntactic using grammar. Frequency method is used to rank words based on how many times the words were typed. Probability table is a table designed for storing data such as predefined phrases and trained data. N-gram is used to train data so that it is able to predict the next word based on previous word. And syntactic using grammar will predict the next
word based on syntactic relationship between previous word and next word. By using this combination, user can reduce the keystroke up to 59% in which the average keystrokes saving is about 50%.
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