Text mining by Tsallis entropy

Jamaati, Maryam; Mehri, Ali

doi:10.1016/j.physa.2017.09.020

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Tsallis entropy has found applications in various diverse domains such as biological, social, financial, networks, and text mining. 26,30,31 Tsallis entropy 30 is given by…”

Section: Tsallis Entropymentioning

confidence: 99%

On the analysis of power law distribution in software component sizes

Sharma

Pendharkar

2021

J Software Evolu Process

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Component-based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen-Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component-based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model.

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“…Tsallis entropy has found applications in various diverse domains such as biological, social, financial, networks, and text mining. 26,30,31 Tsallis entropy 30 is given by…”

Section: Tsallis Entropymentioning

confidence: 99%

On the analysis of power law distribution in software component sizes

Sharma

Pendharkar

2021

J Software Evolu Process

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“…Jamaati and Mehri (2018) suggested the use of Tsallis entropy for extracting keywords. They considered the spatial probability distribution of a word w .that appears n w .times in the text as…”

Section: Tsallis Entropymentioning

confidence: 99%

A Comparative Evaluation of Different Keyword Extraction Techniques

Bisht

2021

International Journal of Information Retrieval Research

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Retrieving keywords in a text is attracting researchers for a long time as it forms a base for many natural language applications like information retrieval, text summarization, document categorization etc. A text is a collection of words that represent the theme of the text naturally and to bring the naturalism under certain rules is itself a challenging task. In the present paper, the authors evaluate different spatial distribution based keyword extraction methods available in the literature on three standard scientific texts. The authors choose the first few high-frequency words for evaluation to reduce the complexity as all the methods are somehow based on frequency. The authors find that the methods are not providing good results particularly in the case of the first few retrieved words. Thus, the authors propose a new measure based on frequency, inverse document frequency, variance, and Tsallis entropy. Evaluation of different methods is done on the basis of precision, recall, and F-measure. Results show that the proposed method provides improved results.

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“…The non-extensivity measure has also been used as a method of identifying important sequences of symbols such as important words in a text (Jamaati and Mehri, 2017) or functional DNA (Moghaddasi et al, 2017). Functional is used to distinguish sections of DNA that are critical for development.…”

Section: Analysis Of Experimental Datamentioning

confidence: 99%

Temporal Structure in Haptic Signaling Under a Cooperative Task

Thorne

Honisch

Kondo

et al. 2019

Front. Hum. Neurosci.

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Haptic communication between humans plays an important role in society. Although this form of communication is ubiquitous at all levels of society and of human development, little is known about how synchronized coordination of motion between two persons leads to higher-order cognitive functions used in communication. In this study, we developed a novel experimental paradigm of a coin-collecting task in which participants used their hands to control a rod to jointly collect the coins on the screen. We characterized the haptic interactions between paired participants while they were taking part in a cooperative task. The individual participants first completed this task on their own and then with a randomly assigned partner for the cooperative task. Single participant experiments were used as a baseline to compare results of the paired participants. Forces applied to the rod were translated to four possible haptic states which encode the combination of the haptic interactions. As a next step, pairs of consecutive haptic states were then combined into 16 possible haptic signals which were classified in terms of their temporal patterns using a Tsallis q-exponential function. For paired participants, 80% of the haptic signals could be fit by the Tsallis q-exponential. On the other hand, only 30% of the signals found in the single-participant trials could be fit by the Tsallis q-exponential. This shows a clear difference in the temporal structures of haptic signals when participants are interacting with each other and when they are not. We also found a large difference in the number of haptic signals used by paired participants and singles. Single participants only used 1/4 of the possible haptic signals. Paired participants, on the other hand, used more than half of the possible signals. These results suggest that temporal structures present in haptic communication could be linked to the emergence of language at an evolutionary level.

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Text mining by Tsallis entropy

Cited by 12 publications

References 22 publications

On the analysis of power law distribution in software component sizes

On the analysis of power law distribution in software component sizes

A Comparative Evaluation of Different Keyword Extraction Techniques

Temporal Structure in Haptic Signaling Under a Cooperative Task

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