Improved Rapid Automatic Keyword Extraction for Voice-based Mechanical Arm Control

Shih, Chi-Huang; Lin, Cheng‐Jian; Jeng, Shiou-Yun

doi:10.18494/sam.2021.3266

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…RAKE was created due to the discovery that keywords frequently include compound words but lack conjunctions and stop words. RAKE bases its automated keyword generation process on a database of conjunctions (Rose et al, 2012;Shih et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

ICD-10 WHO Search With RAKE Algorithm

Wijayakusuma,

Gautama

2023

JMAT

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In most cases, clinicians do not use the ICD-10 standard established by the WHO for diagnosing diseases. These issues may result in outcomes that are undesirable from the standpoints of patient safety and the law. The WHO ICD-10 standard collection of diagnoses that can’t be effectively searched using MySQL’s native search mechanism. Therefore, in order to automatically produce several keywords for each ICD-10 code, academics are interested in analyzing the natural language analysis of WHO ICD-10 data. When diagnosing illnesses, it is envisaged that the availability of numerous types of keywords can lead to more fruitful search results. Natural language analysis, a technique for removing stop words from sentences and simultaneously assessing the semantics of the language from which the keywords will be extracted, makes it possible to do this. Keywords: Natural Language Analysis ; ICD-10 ; Stop Words ; Semantics.

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Section: Methodsmentioning

confidence: 99%

ICD-10 WHO Search With RAKE Algorithm

Wijayakusuma,

Gautama

2023

JMAT

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“…Keywords are the smallest unit that can characterize the topic of a document, and automatic keyword extraction refers to the technique of automatically extracting certain words or phrases representing the document from the document, which has a wide range of applications in natural language processing tasks such as document clustering, text summarization, news recommendation and so on. Automatic keyword extraction techniques can be classified into unsupervised, semi-supervised, and supervised methods based on whether or not they require a training precursor library that already contains labeled keywords [22][23].…”

Section: Automatic Keyword Extraction Techniquesmentioning

confidence: 99%

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

2023

Applied Mathematics and Nonlinear Sciences

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Based on the background of education informatics, this paper uses natural language processing technology to analyze the teaching effect in the Chinese language and literature. By analyzing the basic methods of processing natural language, the CBOW model is constructed using text vectorization. Combined with the LDA model for Chinese language literature text, a keyword is automatically extracted. GRU is chosen as the master control unit of the information feature loop. The CBOW model is optimized using the gradient descent optimization algorithm, which improves the analysis efficiency of education and teaching. Relevant strategies for improving humanistic quality are proposed by analyzing the humanistic quality of Chinese language and literature majors in colleges and universities using natural language processing technology. The results show that the CBOW model performs better in analyzing the textual features of Chinese language and literature, and its MR value is 0.8007±0.0028 compared with the traditional neural network models such as RNN, CNN, etc. Among the strategies for improving humanistic qualities under the education of Chinese language and literature, the effect of strengthening the dissemination of cultural knowledge is 0.8. This study promotes the education of Chinese language and literature in colleges and universities to a certain degree, which is It is conducive to improving students’ humanistic literacy.

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“…In [ 21 ], an enhancement of the term weighting was proposed particularly in the form of a series of modified term frequency-inverse document frequencies, for improving keyword extraction. In [ 22 ], the authors proposed an improved rapid automatic keyword extraction method, which used the word string matching feature in the dictionary method to correspond to the relevant execution action function. In [ 23 ], a novel text mining approach based on keyword extraction and topic modeling was introduced to identify key concerns and their dynamics of on-site issues for better decision-making process.…”

Section: Related Workmentioning

confidence: 99%

Management and Plan of Undergraduates’ Mental Health Based on Keyword Extraction

Zhang

2021

Journal of Healthcare Engineering

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Mental health issues are alarmingly on the rise among undergraduates, which have gradually become the focus of social attention. With the emergence of some abnormal events such as more and more undergraduates’ suspension, and even suicide due to mental health issues, the social attention to undergraduates’ mental health has reached a climax. According to the questionnaire of undergraduates’ mental health issues, this paper uses keyword extraction to analyze the management and plan of undergraduates’ mental health. Based on the classical TextRank algorithm, this paper proposes an improved TextRank algorithm based on upper approximation rough data-deduction. The experimental results show that the accurate rate, recall rate, and F1 of proposed algorithm have been significantly improved, and the experimental results also demonstrate that the proposed algorithm has good performance in running time and physical memory occupation.

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Improved Rapid Automatic Keyword Extraction for Voice-based Mechanical Arm Control

Cited by 3 publications

References 12 publications

ICD-10 WHO Search With RAKE Algorithm

ICD-10 WHO Search With RAKE Algorithm

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

Management and Plan of Undergraduates’ Mental Health Based on Keyword Extraction

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