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        <title>Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph</title>
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        <namePart type="given">Zheng</namePart>
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            <title>Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)</title>
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    <abstract>Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.</abstract>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph
%A Zhang, Zheng
%A Zweigenbaum, Pierre
%A Yin, Ruiqing
%Y Glavaš, Goran
%Y Somasundaran, Swapna
%Y Riedl, Martin
%Y Hovy, Eduard
%S Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F zhang-etal-2018-efficient
%X Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.
%R 10.18653/v1/W18-1702
%U https://aclanthology.org/W18-1702
%U https://doi.org/10.18653/v1/W18-1702
%P 7-11

Markdown (Informal)

[Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph](https://aclanthology.org/W18-1702) (Zhang et al., TextGraphs 2018)

• Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph (Zhang et al., TextGraphs 2018)

ACL

• Zheng Zhang, Pierre Zweigenbaum, and Ruiqing Yin. 2018. Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph. In Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12), pages 7–11, New Orleans, Louisiana, USA. Association for Computational Linguistics.