Analyzing natural human language from the point of view of dynamic of a complex network

Wachs-Lopes, Guilherme Alberto; Rodrigues, Paulo S.

doi:10.1016/j.eswa.2015.09.020

Cited by 28 publications

(12 citation statements)

References 26 publications

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“…With the introduction of complex network theories, it is plausible to conduct large-scale empirical study into language networks [1] [2]. The recent research works gave the semantic representation about human language by complex network model, which employed the dynamic complex network viewpoint to analyze natural human language [3] [4]. The word co-occurrence analysis method was used as a real application example of language network in the scientific fields [5].…”

Section: Related Workmentioning

confidence: 99%

Topology and semantic based topic dependency structure discovery

Zhao¹,

Manandhar²,

Lei³

2018

Filomat

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As an important enabler in achieving the maximum potential of text data analysis, topic relationship dependency structure discovery is employed to effectively support the advanced text data analysis intelligent application. The proposed framework combines an analysis approach of complex network and the Latent Dirichlet Allocation (LDA) model for topic relationship network discovery. The approach is to identify topics of the text data based on the LDA and to discover the graphical semantic structure of the intrinsic association dependency between topics. This not only exploits the association dependency between topics but also leverages a series of upper-level semantic topics covered by the text data. The results of evaluation and experimental analysis show that the proposed method is effective and feasible. The results of the proposed work imply that the topics and relationships between them can be detected by this approach. It also provides complete semantic interpretation.

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Section: Related Workmentioning

confidence: 99%

Topology and semantic based topic dependency structure discovery

Zhao¹,

Manandhar²,

Lei³

2018

Filomat

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“…More especifically, complex network approaches have proven to be a suitable tool for building expert systems, most notably in social sciences ( Legara, Monterola, & David, 2013;Wachs-Lopes & Rodrigues, 2016 ). In general, complex network architecture is used to build and evaluate prediction models.…”

Section: Introductionmentioning

confidence: 99%

“…The effect of network behavior and topology on model performance is also frequently evaluated ( Óskarsdóttir et al, 2017 ). In the Linguistic area, for example, in which many studies have emerged due to explosive growth of Internet, complex network model for semantic representation of human language presents a behavior of scale-free network ( Wachs-Lopes & Rodrigues, 2016 ). In this context, feature or attribute selection, which search for the best subset of attributes in a dataset, is a useful method for leading to a less redundant data, modeling accuracy improvement and reduced processing time for training expert systems ( Aladeemy, Tutun, & Khasawneh, 2017;Elangovan, Devasenapati, Sakthivel, & Ramachandran, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Disease spreading in complex networks: A numerical study with Principal Component Analysis

Schimit

Pereira

2018

Expert Systems with Applications

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a b s t r a c tDisease spreading models need a population model to organize how individuals are distributed over space and how they are connected. Usually, disease agent (bacteria, virus) passes between individuals through these connections and an epidemic outbreak may occur. Here, complex networks models, like Erdös-Rényi, Small-World, Scale-Free and Barábasi-Albert will be used for modeling a population, since they are used for social networks; and the disease will be modeled by a SIR (Susceptible-Infected-Recovered) model. The objective of this work is, regardless of the network/population model, analyze which topological parameters are more relevant for a disease success or failure. Therefore, the SIR model is simulated in a wide range of each network model and a first analysis is done. By using data from all simulations, an investigation with Principal Component Analysis (PCA) is done in order to find the most relevant topological and disease parameters.

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“…From the network analysis perspective, words can be connected with each other to form word networks semantically, orthography, or phonology according to their meanings, syntactic, spelling, or pronunciation [31]. With the development of complex network theory [32,33], there are some study on the network structures of human language [34][35][36][37][38][39], it was shown that Chinese phrases networks exhibit small world patterns [40]. Some studies observed that the language network built from the co-occurrences of words show the small-world effect and the degree display scale-free distribution [41,42].…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative learning strategies based on word networks

Zhao

Jia

Tang

et al. 2018

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• Quantitative English words learning strategies based on network and usage are proposed.• English words network combining etymology and usage information is constructed and analyzed.• The results show the strategies has significant improvement in learning efficiency and effectiveness. Keywords:Word network Network analysis Quantitative linguistics Vocabulary building Corpus-based linguistic analysis Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

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Analyzing natural human language from the point of view of dynamic of a complex network

Cited by 28 publications

References 26 publications

Topology and semantic based topic dependency structure discovery

Topology and semantic based topic dependency structure discovery

Disease spreading in complex networks: A numerical study with Principal Component Analysis

Quantitative learning strategies based on word networks

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