Insights into relevant knowledge extraction techniques: a comprehensive review

Shahid, Abdul; Afzal, Muhammad Tanvır; Abdar, Moloud; Basiri, Mohammad Ehsan; Zhou, Xujuan; Yen, Neil Y.; Chang, Jia‐Wei

doi:10.1007/s11227-019-03009-y

Cited by 24 publications

(47 citation statements)

References 61 publications

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“…In our case, inputs are the sentences, and the outputs are the sentence categories. We used Rectified linear unit (ReLU) or sigmoid activation function to generate output at each layer ( Shahid et al, 2019 ; Abid et al, 2018 ; Warsi et al, 2020 ). Due to RelU, the output ranges are between 0 and max [0, 1], the sigmoid is assigned to the output layer that produces a probability of output between 0 and 1.…”

Section: Study Frameworkmentioning

confidence: 99%

Assessing English language sentences readability using machine learning models

Maqsood

Shahid

Afzal

et al. 2022

PeerJ Computer Science

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Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

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Section: Study Frameworkmentioning

confidence: 99%

Assessing English language sentences readability using machine learning models

Maqsood

Shahid

Afzal

et al. 2022

PeerJ Computer Science

Self Cite

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“…The reasons for false identification (i.e., 22% from Table 1 ) of in-text citation frequencies were: (1) while citing a reference, authors have added/skipped some characters, and (2) reference in the format such as “( Jinha, 2010 ; Funk et al, 2017 ; Scopus, 2021 ; Gusenbauer, 2019 ; Shahid et al, 2020 ; Shahid et al, 2021 )” where references number 2 to 5 are implicitly cited. Such deficiencies will be addressed in the future.…”

Section: System Evaluationmentioning

confidence: 99%

“…This helped the users to figure out a solution to the well-known problem of considering all citations equally. The in-text citation frequencies have been reported as key attributes in discovering the important/influential citations ( Shahid et al, 2020 ; Shahid et al, 2021 ; Zhu et al, 2015 ). Apart from this, various research studies have reported the importance of in-text citations for various purposes.…”

Section: Introductionmentioning

confidence: 99%

In-text citation’s frequencies-based recommendations of relevant research papers

Shahid

Afzal

Alharbi

et al. 2021

PeerJ Computer Science

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From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.

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“…The paper [20] is a survey paper on knowledge extraction techniques for Big Data and Internet of Things. Authors categorize current literature content as content, metadata, collaborative filtering, and citation-based techniques and identified the strengths and limitation for each approach.…”

Section: Editorial Prefacementioning

confidence: 99%