Correcting real-word spelling errors by restoring lexical cohesion

2005 ◽  
Vol 11 (1) ◽  
pp. 87-111 ◽  
Author(s):  
GRAEME HIRST ◽  
ALEXANDER BUDANITSKY
Keyword(s):  
Semantic Distance ◽  
Real Word ◽  
Spelling Errors ◽  
Lexical Cohesion ◽  
Word Spelling ◽  
Spelling Checker

Spelling errors that happen to result in a real word in the lexicon cannot be detected by a conventional spelling checker. We present a method for detecting and correcting many such errors by identifying tokens that are semantically unrelated to their context and are spelling variations of words that would be related to the context. Relatedness to context is determined by a measure of semantic distance initially proposed by Jiang and Conrath (1997). We tested the method on an artificial corpus of errors; it achieved recall of 23–50% and precision of 18–25%.

scholarly journals Evaluating WordNet-based Measures of Lexical Semantic Relatedness

2006 ◽  
Vol 32 (1) ◽  
pp. 13-47 ◽  
Author(s):  
Alexander Budanitsky ◽  
Graeme Hirst
Keyword(s):  
Information Content ◽  
Semantic Relatedness ◽  
Real Word ◽  
Spelling Errors ◽  
Lexical Semantic ◽  
Distributional Similarity ◽  
Central Resource ◽  
Word Spelling

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.

Correcting real-word spelling errors: A new hybrid approach

2017 ◽  
Vol 33 (3) ◽  
pp. 488-499 ◽  
Author(s):  
Seyed MohammadSadegh Dashti ◽  
Amid Khatibi Bardsiri ◽  
Vahid Khatibi Bardsiri
Keyword(s):  
Hybrid Approach ◽  
Real Word ◽  
Spelling Errors ◽  
Word Spelling

scholarly journals An Exploration of Early Spelling in Kindergarten Children With Hearing Loss

2020 ◽  
pp. 1-13
Author(s):  
Carson Aho ◽  
Krystal L. Werfel
Keyword(s):  
Hearing Loss ◽  
Oral Reading ◽  
Scoring Systems ◽  
Normal Hearing ◽  
Kindergarten Children ◽  
Future Research ◽  
Spelling Error ◽  
Spelling Errors ◽  
Word Spelling ◽  
Children With Hearing Loss

Purpose The purpose of this study was to determine if group differences exist in spelling accuracy or spelling errors between kindergarten children with hearing loss and children with normal hearing loss. Method Participants included 23 kindergarten children with hearing loss and 21 children with normal hearing. All children used spoken English as their primary language, and the children with hearing loss used amplification. Participants completed three single-word spelling assessments, a language assessment, and an oral reading assessment. Spelling was scored holistically and with two linguistic-based scoring systems. Results Children with hearing loss did not differ significantly from children with normal hearing in spelling accuracy or linguistic-based spelling error analyses. Conclusions The current study provides evidence that children with hearing loss in kindergarten do not differ significantly in their spelling errors compared to children with normal hearing, aside from a lower proportion of mental graphemic representation errors. With these data, in combination with previous research conducted, speech-language pathologists can further individualize treatment to focus on these specific error patterns. Additionally, this focus of treatment can help better prepare children with hearing loss for spelling and writing tasks in later grades. Future research should be conducted to determine when in elementary school the differences in spelling errors are initially seen.

scholarly journals Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance

2019 ◽  
Vol 4 (1) ◽  
pp. 33
Author(s):  
Imam Cholissodin ◽  
Arief Andy Soebroto ◽  
Sutrisno Sutrisno
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Central Government ◽  
Spelling Errors ◽  
Audit System ◽  
Government Institutions ◽  
Word Spelling ◽  
Writing Errors ◽  
Government Institution ◽  
Editing Process

Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning

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