Language clustering with word co-occurrence networks based on parallel texts

Liu, Haitao; Jin, Cong

doi:10.1007/s11434-013-5711-8

Cited by 85 publications

(70 citation statements)

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“…Furthermore, in [10] they introduced the node selectivity measure that can distinguish the difference between normal and randomised text. Liu and Cong [14] constructed co-occurrence networks from text in different languages and used complex network parameters for the classification (hierarchical clustering) of 14 languages, where Croatian was amongst 12 Slavic. Different applications of linguistic network analysis in NLP includes: evaluation of language complexity [15], automatic summarisation [16] and evaluation of machine translation [17], authorship attribution [18] and text quality analysis [19].…”

Section: Related Workmentioning

confidence: 99%

“…Various types of linguistic networks have already been studied: syntax networks [1,2], semantic networks [3], phonological networks [4], syllable networks [5,6], word co-occurrence networks [7][8][9][10][11][12][13][14][15][16][17][18][19]. In [3,20,21] a systematic methodological overview of linguistic complex networks principles is presented.…”

Section: Introductionmentioning

confidence: 99%

“…In [3,20,21] a systematic methodological overview of linguistic complex networks principles is presented. Recently, linguistic co-occurrence networks have been intensively studied in order to analyse the structure of the language [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the language networks from literature and blogs

Sisovic

Martinčić-Ipšić

Meštrović

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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-In this paper we present the comparison of the linguistic networks from literature and blog texts. The linguistic networks are constructed from texts as directed and weighted co-occurrence networks of words. Words are nodes and links are established between two nodes if they are directly co-occurring within the sentence. The comparison of the networks structure is performed at global level (network) in terms of: average node degree, average shortest path length, diameter, clustering coefficient, density and number of components. Furthermore, we perform analysis on the local level (node) by comparing the rank plots of in and out degree, strength and selectivity. The selectivity-based results point out that there are differences between the structure of the networks constructed from literature and blogs.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of the language networks from literature and blogs

Sisovic

Martinčić-Ipšić

Meštrović

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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“…So far, much research has been carried out, mainly concerned with the structure of syntactic dependency networks (Ferrer i Cancho 2005;Liu 2008;Chen and Liu 2011;Čech et al 2011), the patterns in syntactic dependency networks (Ferrer i Cancho et al 2004;, language development or language evolution (Ke and Yao 2008;Mukherjee et al 2013;Mehler et al 2011), language clustering and linguistic categorization (Liu 2010;Liu and Cong 2013;Gong et al 2012;Abramov and Mehler 2011), manual and machine translation (Amancio et al 2008;Amancio et al 2011), word sense disambiguation (Christiano and Raphael 2013), communication and interaction (Banisch et al 2010;Mehler et al 2010), the structure of semantic networks (Borge-Holthoefer and Arenas 2010; Liu 2009), phonetics (Arbesman et al 2010;Yu et al 2011), morphology (Čech andMačutek 2009;Liu and Xu 2011), parts of speech (Ferrer i Cancho et al 2007), Knowledge Networks (Allee 2007), cognitive networks (Mehler et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Function Nodes in Chinese Syntactic Networks

Chen

Liu

2016

Understanding Complex Systems

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Abstract. Based on two syntactic dependency networks derived from two Chinese treebanks of different registers, a statistical study is conducted regarding word frequency and distributions. We chose three grammatical (function) words as our research objects and analyzed their network features, including degree, out-degree, in-degree, closeness, in-closeness, out-closeness and betweenness. Then we removed these three word nodes from the networks so as to see what consequences may follow in the number of vertices, average degree, average path length, diameter, the number of isolated vertices, domain and density. The results showed that all three function words are central nodes of the Chinese syntactic networks but have different status, since their influence to the overall structure is quite different. The research provides not only a new way for the study on Chinese function words but also a method for examining the influence of node characteristics to a complex network.

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“…Marco teórico y pregunta de la investigación Con el desarrollo de la tecnología informática se introduce la teoría de redes complejas (complex network) en la investigación lingüística (Ferrer i Cancho 2005;Liu 2008;Chen y Liu 2011;Liu y Cong 2013), lo que hace posible convertir el sistema lingüístico en una red compleja. Esa red compleja se compone de nodos y enlaces, y se emplea para hacer un análisis cuantitativo de manera precisa sobre las características de algún sistema lingüístico.…”

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Calificación del test oral EEE-4: análisis del discurso basado basado en redes complejas

李戈

2016

CLAC

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ResumenCon el desarrollo de la informática, en la investigación del lenguaje se introdujo la teoría y metodología de redes complejas, que transforma el sistema de la lengua en las redes complejas compuestas de nodos y enlaces para hacer un análisis cuantitativo de la estructura de la lengua. El desarrollo de la gramática de dependencias proporciona un apoyo teórico a la construcción del corpus anotado (treebank), por lo que el análisis estadístico con las redes complejas se hace posible.Este artículo presenta la teoría y metodología de las redes complejas y construye las redes sintácticas de dependencia a base del corpus anotado (treebank) de las expresiones li: test oral 213 orales del examen EEE-4 (Examen del Español como Especialidad -Nivel 4). Mediante el análisis de las características generales de las redes, incluyendo el número de nodos, los enlaces, el grado medio, la longitud media de los caminos, la distribución de grados y la centralización, tiene como objetivo descubrir la diferencia y similitud potencial entre las expresiones orales de distintos niveles. Además, con el análisis de conglomerados, esta investigación pretende demostrar la capacidad discriminatoria de las variables de las redes complejas y proporcionar una referencia potencial para el trabajo de calificación.Palabras clave: Red compleja, EEE-4, test oral, calificación Abstract Assessment of the EEE-4 oral test: a discourse analysis based on complex networks.With the development of information technology, the theory and methodology of complex network has been introduced to the language research, which transforms the system of language in a complex networks composed of nodes and edges for the quantitative analysis about the language structure. The development of dependency grammar provides theoretical support for the construction of a treebank corpus, making possible a statistic analysis of complex networks. This paper introduces the theory and methodology of the complex network and builds dependency syntactic networks based on the treebank of speeches from the EEE-4 oral test. According to the analysis of the overall characteristics of the networks, including the number of edges, the number of the nodes, the average degree, the average path length, the network centrality and the degree distribution, it aims to find in the networks potential difference and similarity between various grades of speaking performance.Through clustering analysis, this research intends to prove the network parameters' discriminating feature and provide potential reference for scoring speaking performance.

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Language clustering with word co-occurrence networks based on parallel texts

Cited by 85 publications

References 20 publications

Comparison of the language networks from literature and blogs

Comparison of the language networks from literature and blogs

Function Nodes in Chinese Syntactic Networks

Calificación del test oral EEE-4: análisis del discurso basado basado en redes complejas

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