{"id":{"repo_id":"london-metro","oai_identifier":"oai:repository.londonmet.ac.uk:7605"},"canonical_url":"https://search.dev.ndltd.org/etd/london-metro/oai:repository.londonmet.ac.uk:7605","repository":{"repo_id":"london-metro","name":"London Metropolitan University","base_url":"https://repository.londonmet.ac.uk/cgi/oai2"},"display":{"title":"Noisy language modeling framework using neural network techniques","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Li, Jun"],"institution":"London Metropolitan University","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009","date_published":"2009","updated_at":"2026-07-24T02:54:41Z","subjects":["000 Computer science, information & general works"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.grantnumber","label":"Dc Identifier Grantnumber","values":["N/A"],"render_values":[{"text":"N/A","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.sponsor","label":"Sponsor","values":["London Metropolitan University"]},{"key":"dc:creator","label":"Author","values":["Li, Jun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2009"]},{"key":"dc:date.issued","label":"Date","values":["2009"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Computing and Digital Media (SCDM)","Faculty of Computing"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["London Metropolitan University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://repository.londonmet.ac.uk/7605/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["000 Computer science, information & general works"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.grantnumber","label":"Dc Identifier Grantnumber","values":["N/A"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repository.londonmet.ac.uk/7605/1/507073.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Noisy language modeling framework using neural network techniques"]}]}],"canonical_facts":{"dc:contributor.sponsor":["London Metropolitan University"],"dc:creator":["Li, Jun"],"dc:date":["2009"],"dc:date.issued":["2009"],"dc:description.abstract":["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. 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