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Li, Jun (2009) Noisy language modeling framework using neural network techniques. Doctoral thesis, London Metropolitan University.
The text entry interaction between human and computer could be noisy. For example, the typing stream is a reflection of user typing behaviours which include user particular vocabulary, typing habits and typing performance. As computer users inevitably make errors, a typing stream generated from using computer QWERTY keyboard implies all users' self-rectification actions rather that a clean text. Therefore this research develops a novel intermediate layer language modeling framework called ALMIL (i. e. Adaptive Language Modelling Intermediate Layer) which is seen as a communication language layer between human and computer to analyze noisy language stream and provide users with two fundamental functions, namely Text Prediction and Text Correction. A specific research case of ALMIL called Intelligent Keyboard (IK) aiming to develop a user oriented hybrid framework with self-adaptive function to help people using QWERTY keyboard more effectively is also conducted.
In order to explore the methodologies, influential factors and demonstrate the feasibility of the frameworks, a comprehensive neural networks and language modeling process is carried out. Several neural network models which include a Focused Time-Delay Neural Network model (FTDNN) to model non-noisy, noisy and typing stream datasets, a Time Gap Neural Network model (TGNN) to simulate and predict user typing time gap between two consecutive letters, a Prediction using Time Gap model (PTG) to predict right symbols based on user typing speed, a Probabilistic Neural Network based model (PNN) to simulate 'Hitting Adjacent Key Effors', and a Word List real-time ranking model (VvLR) on prioritizing prediction results are developed. All the models have been demonstrated, and shown high performance through a set of experiments using a range of dataset.
In essence, this research brings forth a creative concept - intermediate layer language modeling framework for noisy language processing, pioneers a comprehensive neural networks modelling process, and originally develops a hybrid solution to combine multiple correction functions based on an evolutionary ranking approach. It produces a significant contribution in the area of neural networks application and shows a direction for Human-Computer noisy language interaction research. Also a full report on disabled people typing behaviour, a development of EIM application and a universal pre-processing tool for all neural networks modelling and n-gram, calculation will benefit both research and commerce.
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