Graph-based clustering of extracted paraphrases for labelling crime reports

Das, Priyanka; Das, Asit Kumar

doi:10.1016/j.knosys.2019.05.004

Cited by 35 publications

(13 citation statements)

References 21 publications

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“…Moreover, text mining professionals are increasingly becoming high in demand. Furthermore, text mining may have the power to deliver significant insights to society and individuals, especially with respect to public health [258,259], healthcare [260,261], and education [262][263][264][265], and help evaluate social issues, such as crime (including cybercrime) [245,266,267], child abuse [268], and poverty [269]. Nevertheless, actions must be taken in time to efficiently solve the legal, ethical, and privacy concerns contained in the use of personal data.…”

Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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“…is example will run through the full text to illustrate the specific implementation of the method in this article. Suppose there are the following two sentences a and b, which are regarded as standard sample sentences [30][31][32].…”

Section: Postprocessing Of Sentence Element Attributementioning

confidence: 99%

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Liu

Sharma

et al. 2021

Mathematical Problems in Engineering

119

108

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Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

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“…The models based on clustering are used for processing diagnostic images and for categorising different medical information such as swallowing accelerometery data [4,5]. Also, in crime investigation [3], clustering techniques have been extensively customised to process huge stacks of crime documents to extract useful information relevant to solving problems. This research exploits the unsupervised learning category involving a cluster analysis based on blindly grouping data without having any benchmark output results a priori Worthy of note in the development of data-driven technologies is the collection of heterogeneous data with complex high dimensional attributes.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of K-Means and Traversal Optimisation Algorithms

Adama

Olatunji

Yahaya

et al. 2021

Advances in Intelligent Systems and Computing

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This research aims to present a technical analysis of the Traversal Optimisation Algorithm (TOA) for clustering and K-means clustering algorithm. The goal is to rigorously test this algorithm against different data specifications beyond what has previously been used with K-means without artificially and subjectively setting the initial number of clusters. The experimental evaluation involve the use of diverse cluster optimisation techniques for K-means while applying a wider range of internal validation methods such as Davies-Bouldin Index, Dunn Index and Silhouette Method, for appraising cluster quality of the Traversal Optimisation Algorithm, while at the same time not compromising the configuration of the default algorithm. The findings in this work shows that the optimisation algorithm's clustering quality as calculated by multiple internal validity indices can be very poor when operating on datasets with varying characteristics. This is owing to the algorithm's lack of any add-on mechanism for computing the optimal number of clusters that a dataset needs apriori. The results reveal that in a data processing contexts where the number of clusters are specified, the TOA yields a favourable cost-benefit in terms of run-time complexity and clustering quality.

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Graph-based clustering of extracted paraphrases for labelling crime reports

Cited by 35 publications

References 21 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Comparative Analysis of K-Means and Traversal Optimisation Algorithms

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