Character-Based N-gram Model for Uyghur Text Retrieval

2018 ◽  
pp. 678-688
Author(s):  
Turdi Tohti ◽  
Lirui Xu ◽  
Jimmy Huang ◽  
Winira Musajan ◽  
Askar Hamdulla
Keyword(s):  
Text Retrieval ◽  
N Gram

Evaluation of n-gram conflation approaches for Arabic text retrieval

2009 ◽  
Vol 60 (7) ◽  
pp. 1448-1465 ◽  
Author(s):  
Farag Ahmed ◽  
Andreas Nürnberger
Keyword(s):  
Text Retrieval ◽  
Arabic Text ◽  
N Gram

scholarly journals N-gram and Local Context Analysis for Persian text retrieval

2007 ◽  
Author(s):  
Abolfazl Aleahmad ◽  
Parsia Hakimian ◽  
Farzad Mahdikhani ◽  
Farhad Oroumchian
Keyword(s):  
Text Retrieval ◽  
Local Context ◽  
Context Analysis ◽  
N Gram

Character N-Gram Tokenization for European Language Text Retrieval

2004 ◽  
Vol 7 (1/2) ◽  
pp. 73-97 ◽  
Author(s):  
Paul McNamee ◽  
James Mayfield
Keyword(s):  
Text Retrieval ◽  
European Language ◽  
N Gram ◽  
Language Text

scholarly journals A Word & Character N-Gram based Arabic OCR Error Simulation model

2014 ◽  
Vol 12 (8) ◽  
pp. 3758-3767 ◽  
Author(s):  
Mostafa Ezzat ◽  
Tarek Ahmed ElGhazaly ◽  
Mervat Gheith
Keyword(s):  
Simulation Model ◽  
High Precision ◽  
Text Retrieval ◽  
Search Query ◽  
New Model ◽  
Retrieval Effectiveness ◽  
Proposed Model ◽  
Arabic Ocr ◽  
N Gram ◽  
Error Simulation

This paper provides a new model aimed to enhanceArabic OCR degraded text retrieval effectiveness. The proposed model based onsimulating the Arabic OCR recognition mistakesbased on both, word based and Character N-Gram approaches. Then we expand the user search query using the expected OCR errors. The resulting search query expanded gives high precision and recall values in searching Arabic OCR-Degraded text rather than the original query. The proposed model showed a significant increase in the degraded text retrieval effectiveness over the previous models. The retrieval effectiveness of the newmodel is %93, while the best effectiveness published for word based approach was %84 and the best effectiveness for character based approach was %56.

N-gram based Language Model for the QWERTY Keyboard Input Errors in a Touch Screen Environment

2018 ◽  
Vol 7 (2) ◽  
pp. 54-59
Author(s):  
Yoon Gee Ong ◽  
◽  
Seung Shik Kang ◽  
Keyword(s):  
Language Model ◽  
Touch Screen ◽  
Keyboard Input ◽  
N Gram

Learning N-Gram Language Models from Uncertain Data

2016 ◽  
Author(s):  
Vitaly Kuznetsov ◽  
Hank Liao ◽  
Mehryar Mohri ◽  
Michael Riley ◽  
Brian Roark
Keyword(s):  
Uncertain Data ◽  
Language Models ◽  
N Gram

Rescore in a Flash: Compact, Cache Efficient Hashing Data Structures for n-Gram Language Models

2020 ◽  
Author(s):  
Grant P. Strimel ◽  
Ariya Rastrow ◽  
Gautam Tiwari ◽  
Adrien Piérard ◽  
Jon Webb
Keyword(s):  
Data Structures ◽  
Language Models ◽  
Cache Efficient ◽  
N Gram

scholarly journals OCR correction for Indonesian historic newspapers using word repetition, stemmer and n-gram

2019 ◽  
Vol 1193 ◽  
pp. 012032
Author(s):  
D Purwantoro ◽  
H Akbar ◽  
A Hidayati ◽  
Sfenrianto
Keyword(s):  
Word Repetition ◽  
N Gram

Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

2020 ◽  
Vol 12 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Al Hafiz Akbar Maulana Siagian ◽  
Masayoshi Aritsugi
Keyword(s):  
N Gram
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