Complex networks measures for differentiation between normal and shuffled Croatian texts

Margan, Domagoj; Meštrović, Ana; Martinčić-Ipšić, Sanda

doi:10.1109/mipro.2014.6859820

Cited by 13 publications

(18 citation statements)

References 9 publications

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“…In [50] our results showed that in-selectivity and out-selectivity values from shuffled texts are constantly below selectivity values calculated from normal texts. It seems that selectivity measure is able to capture typical word phrases and collocations which are lost during the shuffling procedure.…”

Section: Introductioncontrasting

confidence: 54%

“…We employed various methods from the LaNCoA toolkit for calculating the network measures and generating various plots in order to find the differences between two classes of networks. In [50] we extended this research by introducing additional shuffling procedure: the sentence-level shuffling procedure and by introducing a node selectivity as a new complex network measure. All shuffling procedures and network construction tasks were performed with the LaNCoA toolkit.…”

Section: The Lancoa Toolkit Applicationsmentioning

confidence: 99%

“…These features position Croatian among morphologically rich and free word-order languages. So far Croatian has been quantified in a complex networks framework based on the word cooccurrences [14,18] and compared with shuffled counterparts [21,50].…”

Section: Introductionmentioning

confidence: 99%

“…Selectivity is defined as the average weight distribution on the links of the single node. In our previous work, the node selectivity measure performed in favour of the differentiation between original and shuffled Croatian texts [21,50], and for the differentiation of blog and literature text genres [71]. In this work we explore the potential of the selectivity for the keyword extraction in the Croatian news articles.…”

Section: Introductionmentioning

confidence: 99%

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Language networks

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The language networks book provides insights into the principles of modeling and analyzing structural properties of language-manly in its written form, hence text. Book guidelines the basic principles of text preprocessing, covering the very initial steps needed for any natural language processing task. Further, the book examines the possibilities of representing text in a complex networks framework. The second part overviews the application of language networks as one of a data science disciplines. It covers important data science topics for the processing of the big textual data from extracting the most salient structural parts of documents, across differentiation between text genres to predicting the speeding of the information through social media. Finally, the last part of the book is tasked with formal modeling of the linguistic subsystems in a multilayer complex networks formalism, which allows systematic study of language across all of its subsystems. The first part of the book studies the general principles of language networks construction and analysis. It covers language network construction types. Specifically, it analyzes the effects of constructing directed vs. undirected, weighted vs. unweighted network from lemmatized (stemmed) or non-lemmatized texts with stopwords included or excluded. The effects of text randomization are studied enabling better insights into characteristics of language networks compared to their shuffled counterparts. Some preliminary experiments reveal the possibilities of the differentiation of the structural properties of networks constructed from different text types and in different languages like Croatian, English, and Italian. Next, some initial insights into the characterization of syllabic networks are presented. The analysis of motifs of the linguistic networks reveals the typical building blocks of the structure of networks of the literature in the Croatian language. Finally, the first part of the book concludes with the LaNCoA a Python Toolkit for the construction and analysis of language networks implementing the majority of the findings presented in this part of the book. The second part of the book is dedicated to the applications of language networks. The language networks enable the extraction of the most salient words in texts-keywords and extraction of the domain knowledge-context studied on the content of Wikipedia entries. The applicative part of language networks includes the differentiation between different text types and polarization of tweets, as well. Finally, the possibilities of predicting the future content of tweets solely from the structural properties of the complex language networks are presented. Contents I Language Networks Construction

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Section: Introductioncontrasting

confidence: 54%

Section: The Lancoa Toolkit Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Language networks

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show abstract

“…These features position Croatian among morphologically rich and free word-order languages. So far Croatian has been quantified in a complex networks framework based on the word co-occurrences [7], [1] and compared with shuffled counterparts [8], [9].…”

Section: Introductionmentioning

confidence: 99%