Grammar Error Detection Tool for Medical Transcription Using Stop Words Parts-of-Speech Tags Ngram Based Model

Ganesh, B R; Gupta, Deepa; Sasikala, T.

doi:10.1007/978-981-10-8228-3_5

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…The statistical approach includes techniques like machine learning, pattern matching and mining. (Ganesh, Gupta & Sasikala, 2018;Schmaltz et al, 2016;Islam et al, 2018;Xiang et al, 2015;Zheng et al, 2016;Yeh, Hsu & Yeh, 2016;Ferraro et al, 2014) Ge, Wei & Zhou, 2018;Zhao et al, 2019;Yannakoudakis et al, 2017;Felice & Briscoe, 2015;Wang et al, 2014;Xiang et al, 2015;Zheng et al, 2016;Yeh, Hsu & Yeh, 2016;Ferraro et al, 2014;Sonawane et al, 2020;Zan et al, 2020;Agarwal, Wani & Bours, 2000;Maghraby et al, 2020) (Yeh, Hsu & Yeh, 2016;Shiue, Huang & Chen, 2017;Yu & Chen, 2012;Cheng, Yu & Chen, 2014;Rei & Yannakoudakis, 2016;Cheng, Fang & Ostendorf, 2017;Ferraro et al, 2014;Islam et al, 2018;Zheng et al, 2016;Xiang et al, 2015;Zan et al, 2020;Agarwal, Wani & Bours, 2000) Hybrid approach…”

Section: Statistical Approachmentioning

confidence: 99%

“…This establishes the true positive and true negative identification cases are better in our approach for detecting the errors and thus the correctness or incorrectness of the question sentences. Full-size  DOI: 10.7717/peerj-cs.532/fig- 6 Table 5 Comparative results of the proposed approach and the solution given in (Ganesh, Gupta & Sasikala, 2018).…”

Section: Experiments and Performance Evaluation For Question's Correctness Assessmentmentioning

confidence: 99%

“…In another approach, Tezcan, Hoste & Macken (2016) proposed a rule-based dependency parser that queries a treebank for detecting sentence structure error. In the statistical approach, n-gram based ( Ganesh, Gupta & Sasikala, 2018 ) and machine learning based ( Schmaltz et al, 2016 ) techniques are followed to determine the errors. Islam et al (2018) proposed sequence to sequence learning model which uses encoder-decoder architecture for resolving missing word error and incorrect arrangement of words in the sentence.…”

Section: Related Workmentioning

confidence: 99%

“…A collocation match in a corpus would able to detect collocation errors. Besides machine learning models, a statistical model based on sequential word pattern mining has been quite effective in detecting grammatical errors ( Ganesh, Gupta & Sasikala, 2018 ). Statistical modeling and machine learning, though easy to implement, are sometimes outperformed by rule-based techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The result of the proposed approach is compared with the results of another similar work by Ganesh, Gupta & Sasikala (2018) , in which the authors applied a tri-gram based approach to detect an error in English language sentences. Table 5 shows the result comparison in terms of four assessment metrics.…”

Section: Assessing the Correctness Of The Learners' Input Questionsmentioning

confidence: 99%

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Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Pal

Pramanik

Maity

et al. 2021

PeerJ Computer Science

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In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner’s query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy.

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Section: Statistical Approachmentioning

confidence: 99%

Section: Experiments and Performance Evaluation For Question's Correctness Assessmentmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Assessing the Correctness Of The Learners' Input Questionsmentioning

confidence: 99%

See 3 more Smart Citations

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Pal

Pramanik

Maity

et al. 2021

PeerJ Computer Science

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show abstract

Automatic Correction of Speech Recognized Mathematical Equations using Encoder-Decoder Attention Model

Mounika

Tarakaram

Prasanna

et al. 2022

2022 IEEE 19th India Council International Conference (INDICON)

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Automatic construction of generic stop words list for hausa text

Bichi

Samsudin

Hassan

2022

IJEECS

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<span lang="EN-US">Stop-words are words having the highest frequencies in a document without any significant information. They are characterized by having common relations within a cluster. They are the noise of the text that are evenly distributed over a document. Removal of stop words improve the performance and accuracy of information retrieval algorithms and machine learning at large. It saves the storage space by reducing the vector space dimension, and helps in effective documents indexing. This research generated a list of Hausa stop words automatically using aggregated method by combining frequency and statistics methods. The experiments are conducted using a primarily collected Hausa corpus consisting of 841 Hausa news articles of size 646862 words and finally a list of distinct 81 Hausa stop words is generated.</span>

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Grammar Error Detection Tool for Medical Transcription Using Stop Words Parts-of-Speech Tags Ngram Based Model

Cited by 4 publications

References 12 publications

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Automatic Correction of Speech Recognized Mathematical Equations using Encoder-Decoder Attention Model

Automatic construction of generic stop words list for hausa text

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