Open AccessOn Combining Language Models to Improve a Text-based Human-machine Interface
Author(s) -
Daniel Cruz Cavalieri,
Teodiano Bastos,
Sira E. Palazuelos-Cagigas,
Mário Sarcinelli-Filho
Publication year - 2015
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/61753
Subject(s) - perplexity , computer science , language model , word (group theory) , interpolation (computer graphics) , interface (matter) , n gram , task (project management) , artificial intelligence , natural language processing , speech recognition , motion (physics) , linguistics , philosophy , management , bubble , maximum bubble pressure method , parallel computing , economics
This paper concentrates on improving a text-based human-machine interface integrated into a robotic wheelchair. Since word prediction is one of the most common methods used in such systems, the goal of this work is to improve the results using this specific module. For this, an exponential interpolation language model (LM) is considered. First, a model based on partial differential equations is proposed; with the appropriate initial conditions, we are able to design a interpolation language model that merges a word-based n-gram language model and a part-of-speech-based language model. Improvements in keystroke saving (KSS) and perplexity (PP) over the word-based n-gram language model and two other traditional interpolation models are obtained, considering two different task domains and three different languages. The proposed interpolation model also provides additional improvements over the hit rate (HR) parameter