TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Rabby, Gollam; Azad, Saiful; Mahmud, Mufti; Zamli, Kamal Z.; Rahman, Mahbubur

doi:10.1007/s12559-019-09706-3

Cited by 97 publications

(52 citation statements)

References 59 publications

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“…From the review of literature, we understand that in these days, researchers are employing machine learning (ML) in a variety of tasks including biological data mining [ 9 , 10 ], image analysis [ 11 , 27 ], financial forecasting [ 12 ], anomaly detection [ 13 – 15 ], disease detection [ 16 , 17 ], natural language processing [ 18 , 19 ], and assay detection [ 20 ]. More recent studies on COVID-19 detection using deep learning (DL) models can be seen in the literature [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

et al. 2021

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Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

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Section: Introductionmentioning

confidence: 99%

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

et al. 2021

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“…We employ the following unsupervised automatic Keyphrase extractors used for research documents such as TextRank and RAKE. In the following sections, we discuss how these automatic Keyphrase extractors work [12].…”

Section: Background Workmentioning

confidence: 99%

Keyphrase Extraction from Document Using RAKE and TextRank Algorithms

Baruni¹,

Sathiaseelan²

2020

IJCSMC

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Traditional approaches to extract useful Keyphrase from a sentence rely heavily on human effort. In this paper, to overcome this challenge, Automatic Keyphrase Extraction algorithm has been used to extract a Keyphrase efficiently that reduces the scope for human errors and saves time. The Machine Learning algorithms detect the Keyphrase from a sentence that the user feeds as an input and sets a reminder using the Keyphrase. RAKE and Textrank algorithms help to extract Keyphrase or important terms of a given text document. RAKE and TextRank techniques applied to find and analyze the best possible way of extracting the Keyphrase efficiently. With slight modifications to the code, the algorithms can be implemented to serve different application domain such as message or threat decoding in military purposes and can be extended to use in speech-to-text translation and sentimental analysis of the data.

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“…In recent years, DL methods are potentially reshaping the future of ML and AI [ 48 ]. It is worthy to mention here that, from a broader perspective, ML has been applied to a range of tasks including anomaly detection [ 49 , 50 , 278 , 283 , 290 ], biological data mining [ 51 , 52 ], detection of coronavirus [ 53 , 54 ], disease detection and patient management [ 55 – 57 , 277 , 279 – 282 , 284 , 286 , 287 , 289 , 291 ], education [ 58 ], natural language processing [ 59 , 285 , 288 ], and price prediction [ 60 ]. Despite notable popularity and applicability to diverse disciplines [ 61 ], there exists no comprehensive review which focuses on pattern recognition in biological data and provides pointers to the various biological data sources and DL tools, and the performances of those tools [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Mining Biological Data

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Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

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TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Cited by 97 publications

References 59 publications

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

Keyphrase Extraction from Document Using RAKE and TextRank Algorithms

Deep Learning in Mining Biological Data

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TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Cited by 97 publications

References 59 publications

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

Keyphrase Extraction from Document Using RAKE and TextRank Algorithms﻿

Deep Learning in Mining Biological Data

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Product

Resources

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Keyphrase Extraction from Document Using RAKE and TextRank Algorithms