A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case

Bounabi, Mariem; Elmoutaouakil, Karim; Satori, Khalid

doi:10.1108/ijwis-11-2020-0067

Cited by 15 publications

(8 citation statements)

References 34 publications

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“…Text feature extraction is a fundamental task in text classification, which is divided into two methods: machine learning and deep learning. The machine learning method was usually adopted word frequency statistics, e.g., Term Frequency-Inverse Document Frequency (TF-IDF) [23] can characterize text features by considering the word frequency and inverse document frequency, which can better classify the texts. The machine learning methods cannot mine more profound into the contextual semantic information of text and do not perform well on text classification tasks with high abstraction, such as the threat behavior recognition task in this paper.…”

Section: Text Feature Extraction and Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors' profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue. It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence. With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors. The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques. Extend the experiment to verify the method's effectiveness in identifying the malware and threat actors in APT attacks. The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art. The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

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Section: Text Feature Extraction and Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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“…5 that is a screenshot depicting polarity and subjectivity measures of each statement on downloaded files. The second task of the first phase is applying "neutrosophic term frequencyinverse term frequency (NTF-IDF)", that "is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages" as a text mining approach [47]. We use the hereunder formulation described on the work of Majumdar to extract explicitly out of tacticity using, the extracted polarity, subjectivity, and recency matrix.…”

Section: Fig 2 Research Methodology and Proposed Approachmentioning

confidence: 99%

“…There are plenty of text mining approaches available at the works of Michael et al, [31] and Feng et al, [32] and quite comprehensive number of techniques, methods, tools, libraries and packges that one can get more information reading the work of Ignatow and Mihalcea [33]. The neutrosophic term weighing technique developed by Bounabi et al, is considered as the main text mining analysis method used to execute to be defined methodological approach [34].…”

Section: Background Of Text Miningmentioning

confidence: 99%

A Semiautonomous Approach for Strategic Decision-Making; an Application for Digital Transformation in Finance

Sarvari

Martin

Grybauskas

et al. 2022

Preprint

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This paper proposes an innovative approach to help domain knowledge experts with technology roadmapping and the strategic decision-making process. In our approach, we introduce a retrofitted parallel coordinate visualization tool, which is enhanced with a feature importance analysis algorithm. Our approach has produced four significant achievements. First, our new method reveals the shortcomings of some traditional parallel coordinate tools. Second, our method applies neutrosophic fuzzy sets in tacit knowledge analysis to foster ease with written text data; it assists with the processes in exploring possible roadmapping strategies, measuring vicinity conditions of adjacent features, and prioritizing their order based on the desired solution space of a certain attributes. The methods are applied in a case study with actual data that comes from a research project designed for financial organizations’ digitalization. Third, the application of parallel coordinates is reported for the first time for technology roadmapping in finance. Finally, proof of concept and a benchmarking practice considering generated hypotheses are provided in this paper.

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“…Whether to model language in a connectionist or symbolic manner hinges also on its inherent compositionality. 17 Von Rueden et al [152] propose a taxonomy for integrating prior knowledge into learning systems. This is an extensive work covering types of knowledge and knowledge representations, neuro-symbolic integration approaches, motivations for each approach, challenges and future directions.…”

Section: Related Workmentioning

confidence: 99%

“…Given that our work deals with natural language as input, we are only concerned with logic rules (which we subdivide into rules and logic) and knowledge graphs (which we subdivide into frames and semantic networks) -see Section 6.2.2. 16 https://www.britannica.com/topic/morphology-linguistics 17 According to Noam Chomsky's theory of language, language is compositional, in the sense that a sentence is composed of phrases, which are in turn composed of sub-phrases, and so on, in a recursive manner. This idea enables the construction of infinite possibilities from finite means.…”

Section: Related Workmentioning

confidence: 99%

Is neuro-symbolic AI meeting its promises in natural language processing? A structured review

Hamilton

Nayak

Božić

et al. 2024

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Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.11 https://github.com/kyleiwaniec/neuro-symbolic-ai-systematic-review

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A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case

Cited by 15 publications

References 34 publications

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

A Semiautonomous Approach for Strategic Decision-Making; an Application for Digital Transformation in Finance

Is neuro-symbolic AI meeting its promises in natural language processing? A structured review

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