The role of syntax in complex networks: Local and global importance of verbs in a syntactic dependency network

Čech, Radek; Mačutek, Ján; Žabokrtský, Zdeněk

doi:10.1016/j.physa.2011.05.027

Cited by 23 publications

(11 citation statements)

References 18 publications

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“…Directed and undirected networks differ based on whether they feature directed or undirected edges, respectively. 4 Networks have been shown to be a suitable tool to represent syntactic relations (Liu 2008;Čech & Mačutek 2009;Čech, Mačutek & Žabokrtský 2011;Passarotti 2014;Čech, Mačutek & Liu 2016). This holds particularly true for dependency grammars, which view syntactic structures as binary and hierarchical relations between lexical nodes (Robinson 1970), thereby allowing the representation of sentences as rooted trees.…”

Section: Defining Syntactic Dependency Networkmentioning

confidence: 99%

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

2021

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SIGTYP 2021 is the third edition of the workshop for typology-related research and its integration into multilingual Natural Language Processing (NLP). The workshop is co-located with the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021), which takes place virtually this year. Our workshop includes a shared task on robust language identification from speech.The final program of SIGTYP contains 4 keynote talks, 3 shared task papers, 10 archival papers, and 14 extended abstracts. This workshop would not have been possible without the contribution of its program committee, to whom we would like to express our gratitude. We should also thank Claire Bowern, Miryam de Lhoneux, Johannes Bjerva, and David Yarowsky for kindly accepting our invitation as invited speakers. The workshop is generously sponsored by Google.

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Section: Defining Syntactic Dependency Networkmentioning

confidence: 99%

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

2021

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“…The idea of language as a network has been gaining some traction in computational linguistics (e.g., Ferrer-i Cancho et al, 2007;Lerner et al, 2009;Ke and Yao, 2008;Borge-Holthoefer and Arenas, 2010;Lerner et al, 2009;Choudhury et al, 2010;Ferrer-i Cancho and Solé, 2001;Vitevitch et al, 2011;Ferrer-i Cancho et al, 2004;Čech et al, 2011;Liu and Xu, 2011;Mehler et al, 2016). One approach, that we explore here, is to construct language networks from annotated dependency treebank to encode syntactic relationship between lexical items.…”

Section: Introductionmentioning

confidence: 99%

Can Greenbergian universals be induced from language networks?

Sharma¹,

Swami²,

Shete³

et al. 2019

Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)

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Language networks have been proposed to be the underlying representation for syntactic knowledge (Roelofs, 1992; Pickering and Branigan, 1998). Such networks are known to explain various word order related priming effects in psycholinguistics. Under the assumption that word order information is encoded in these networks, we explore if Greenbergian word order universals (Greenberg, 1963) can be induced from such networks. Language networks for 34 languages were constructed from the Universal Dependencies Treebank (Nivre et al., 2016) based on the assumptions in Roelofs (1992); Pickering and Branigan (1998). We conducted a series of experiments to investigate if certain network parameters can be used to cluster various languages based on the word order typology proposed by Greenberg. Our results show that some network parameters robustly cluster the languages correctly, thereby providing some support for language network as a valid representation for such linguistic generalizations.

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“…In this model, words are modelled as nodes in the network while the edges may represent syntactic [12], semantic [37], or empirical [38] relationships. The complementary role played by co-occurrence networks in text analysis stems from their ability in considering both meso-and large-scale structure of texts, a feature markedly overlooked by bag-ofword models [13].…”

Section: Introductionmentioning

confidence: 99%

Labelled network subgraphs reveal stylistic subtleties in written texts

Marinho

Hirst

Amancio

2017

Journal of Complex Networks

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The vast amount of data and increase of computational capacity have allowed the analysis of texts from several perspectives, including the representation of texts as complex networks. Nodes of the network represent the words, and edges represent some relationship, usually word co-occurrence. Even though networked representations have been applied to study some tasks, such approaches are not usually combined with traditional models relying upon statistical paradigms. Because networked models are able to grasp textual patterns, we devised a hybrid classifier, called labelled motifs, that combines the frequency of common words with small structures found in the topology of the network, known as motifs. Our approach is illustrated in two contexts, authorship attribution and translationese identification. In the former, a set of novels written by different authors is analyzed. To identify translationese, texts from the Canadian Hansard and the European parliament were classified as to original and translated instances. Our results suggest that labelled motifs are able to represent texts and it should be further explored in other tasks, such as the analysis of text complexity, language proficiency, and machine translation.

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The role of syntax in complex networks: Local and global importance of verbs in a syntactic dependency network

Cited by 23 publications

References 18 publications

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

Can Greenbergian universals be induced from language networks?

Labelled network subgraphs reveal stylistic subtleties in written texts

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