Code from the paper: https://link.springer.com/chapter/10.1007/978-3-030-31372-2_10
@InProceedings{10.1007/978-3-030-31372-2_10,
author="{\v{S}}krlj, Bla{\v{z}}
and Pollak, Senja",
editor="Mart{\'i}n-Vide, Carlos
and Purver, Matthew
and Pollak, Senja",
title="Language Comparison via Network Topology",
booktitle="Statistical Language and Speech Processing",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="112--123",
abstract="Modeling relations between languages can offer understanding of language characteristics and uncover similarities and differences between languages. Automated methods applied to large textual corpora can be seen as opportunities for novel statistical studies of language development over time, as well as for improving cross-lingual natural language processing techniques. In this work, we first propose how to represent textual data as a directed, weighted network by the text2net algorithm. We next explore how various fast, network-topological metrics, such as network community structure, can be used for cross-lingual comparisons. In our experiments, we employ eight different network topology metrics, and empirically showcase on a parallel corpus, how the methods can be used for modeling the relations between nine selected languages. We demonstrate that the proposed method scales to large corpora consisting of hundreds of thousands of aligned sentences on an of-the-shelf laptop. We observe that on the one hand properties such as communities, capture some of the known differences between the languages, while others can be seen as novel opportunities for linguistic studies.",
isbn="978-3-030-31372-2"
}