Evolutionary ranking on multiple word correction algorithms using neural network approach

Jun Li; Karim Ouazzane; Yanguo Jing; Hassan Kazemian; Richard Boyd

doi:10.1007/978-3-642-03969-0_38

Evolutionary ranking on multiple word correction algorithms using neural network approach

Jun Li, Karim Ouazzane, Yanguo Jing, Hassan Kazemian, Richard Boyd

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Multiple algorithms have been developed to correct user's typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.

Original language	English
Title of host publication	Engineering applications of neural networks
Subtitle of host publication	11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings
Editors	Dominic Palmer-Brown, Chrisina Draganova, Elias Pimenidis, Haris Mouratidis
Place of Publication	Berlin
Publisher	Springer Nature
Pages	409-418
Number of pages	10
ISBN (Electronic)	9783642039690
ISBN (Print)	9783642039683
DOIs	https://doi.org/10.1007/978-3-642-03969-0_38
Publication status	Published - 17 Aug 2009
Externally published	Yes

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	43
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Keywords

Jaro distance
Jaro-Winkler distance
Levenshtein distance
Metaphone
Neural Network
ranking First Hitting Rate
word 2-gram

Access to Document

10.1007/978-3-642-03969-0_38Licence: Unspecified

Cite this

Li, J., Ouazzane, K., Jing, Y., Kazemian, H., & Boyd, R. (2009). Evolutionary ranking on multiple word correction algorithms using neural network approach. In D. Palmer-Brown, C. Draganova, E. Pimenidis, & H. Mouratidis (Eds.), Engineering applications of neural networks: 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings (pp. 409-418). (Communications in Computer and Information Science; Vol. 43). Springer Nature. https://doi.org/10.1007/978-3-642-03969-0_38

Li, Jun ; Ouazzane, Karim ; Jing, Yanguo et al. / Evolutionary ranking on multiple word correction algorithms using neural network approach. Engineering applications of neural networks: 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings. editor / Dominic Palmer-Brown ; Chrisina Draganova ; Elias Pimenidis ; Haris Mouratidis. Berlin : Springer Nature, 2009. pp. 409-418 (Communications in Computer and Information Science).

@inproceedings{bfd76d10fcf44838937dcfa6811844d8,

title = "Evolutionary ranking on multiple word correction algorithms using neural network approach",

abstract = "Multiple algorithms have been developed to correct user's typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.",

keywords = "Jaro distance, Jaro-Winkler distance, Levenshtein distance, Metaphone, Neural Network, ranking First Hitting Rate, word 2-gram",

author = "Jun Li and Karim Ouazzane and Yanguo Jing and Hassan Kazemian and Richard Boyd",

year = "2009",

month = aug,

day = "17",

doi = "10.1007/978-3-642-03969-0_38",

language = "English",

isbn = "9783642039683",

series = "Communications in Computer and Information Science",

publisher = "Springer Nature",

pages = "409--418",

editor = "Dominic Palmer-Brown and Chrisina Draganova and Elias Pimenidis and Haris Mouratidis",

booktitle = "Engineering applications of neural networks",

address = "Germany",

}

Li, J, Ouazzane, K, Jing, Y, Kazemian, H & Boyd, R 2009, Evolutionary ranking on multiple word correction algorithms using neural network approach. in D Palmer-Brown, C Draganova, E Pimenidis & H Mouratidis (eds), Engineering applications of neural networks: 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings. Communications in Computer and Information Science, vol. 43, Springer Nature, Berlin, pp. 409-418. https://doi.org/10.1007/978-3-642-03969-0_38

Evolutionary ranking on multiple word correction algorithms using neural network approach. / Li, Jun; Ouazzane, Karim; Jing, Yanguo et al.
Engineering applications of neural networks: 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings. ed. / Dominic Palmer-Brown; Chrisina Draganova; Elias Pimenidis; Haris Mouratidis. Berlin: Springer Nature, 2009. p. 409-418 (Communications in Computer and Information Science; Vol. 43).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evolutionary ranking on multiple word correction algorithms using neural network approach

AU - Li, Jun

AU - Ouazzane, Karim

AU - Jing, Yanguo

AU - Kazemian, Hassan

AU - Boyd, Richard

PY - 2009/8/17

Y1 - 2009/8/17

N2 - Multiple algorithms have been developed to correct user's typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.

AB - Multiple algorithms have been developed to correct user's typing mistakes. However, an optimum solution is hardly identified among them. Moreover, these solutions rarely produce a single answer or share common results, and the answers may change with time and context. These have led this research to combine some distinct word correction algorithms to produce an optimal prediction based on database updates and neural network learning. In this paper, three distinct typing correction algorithms are integrated as a pilot research. Key factors including Time Change, Context Change and User Feedback are considered. Experimental results show that 57.50% Ranking First Hitting Rate (HR) with the samples of category one and a best Ranking First Hitting Rate of 74.69% within category four are achieved.

KW - Jaro distance

KW - Jaro-Winkler distance

KW - Levenshtein distance

KW - Metaphone

KW - Neural Network

KW - ranking First Hitting Rate

KW - word 2-gram

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DO - 10.1007/978-3-642-03969-0_38

M3 - Conference contribution

SN - 9783642039683

T3 - Communications in Computer and Information Science

SP - 409

EP - 418

BT - Engineering applications of neural networks

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A2 - Pimenidis, Elias

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Li J, Ouazzane K, Jing Y, Kazemian H, Boyd R. Evolutionary ranking on multiple word correction algorithms using neural network approach. In Palmer-Brown D, Draganova C, Pimenidis E, Mouratidis H, editors, Engineering applications of neural networks: 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings. Berlin: Springer Nature. 2009. p. 409-418. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-03969-0_38

Evolutionary ranking on multiple word correction algorithms using neural network approach

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