Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F22%3A73618988" target="_blank" >RIV/61989592:15210/22:73618988 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/22:00558164

Výsledek na webu

<a href="http://wupes.utia.cas.cz/2022/Proceedings.pdf" target="_blank" >http://wupes.utia.cas.cz/2022/Proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks

Popis výsledku v původním jazyce

A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper we study loanwords in the South-East Asia Archipelago, a home to a large number of languages. Our paper is inspired by the works of Hoffmann et al. (2021) Bayesian methods are applied to probabilistic modeling of family trees representing the history of language families and by Haynie et al. (2014) modelling the diffusion of a special class of loanwords, so called Wanderwörter in languages of Australia, North America and South America. We assume that in the South-East Asia Archipelago Wanderwörter spread along specific maritime trade routes whose geographical characteristics can help unravel the history of Wanderwörter diffusion in the area. For millennia trade was conducted using sailing ships which were constrained by the monsoon system and in certain areas also by strong sea currents. Therefore rather than the geo-graphical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contacts among cultures.We use a sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to travel times of a Chinese map of the sea trade routes from the early seventeenth century. We model the spread of loanwords using a probabilistic graphical model - a Bayesian network. We design a novel heuristic Bayesian network structure learning algorithm that learns the structure as a union of spanning trees for graphs of all loanwords in the training dataset. We compare this algorithm with BIC optimal Bayesian networks by measuring how well these models predict the true presence/absence of a loanword. Interestingly, Bayesian networks learned by our heuristic spanning tree based algorithm provide better results than the BIC optimal Bayesian networks.

Název v anglickém jazyce

Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks

Popis výsledku anglicky

A loanword is a word permanently adopted from one language and incorporated into another language without translation. In this paper we study loanwords in the South-East Asia Archipelago, a home to a large number of languages. Our paper is inspired by the works of Hoffmann et al. (2021) Bayesian methods are applied to probabilistic modeling of family trees representing the history of language families and by Haynie et al. (2014) modelling the diffusion of a special class of loanwords, so called Wanderwörter in languages of Australia, North America and South America. We assume that in the South-East Asia Archipelago Wanderwörter spread along specific maritime trade routes whose geographical characteristics can help unravel the history of Wanderwörter diffusion in the area. For millennia trade was conducted using sailing ships which were constrained by the monsoon system and in certain areas also by strong sea currents. Therefore rather than the geo-graphical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contacts among cultures.We use a sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to travel times of a Chinese map of the sea trade routes from the early seventeenth century. We model the spread of loanwords using a probabilistic graphical model - a Bayesian network. We design a novel heuristic Bayesian network structure learning algorithm that learns the structure as a union of spanning trees for graphs of all loanwords in the training dataset. We compare this algorithm with BIC optimal Bayesian networks by measuring how well these models predict the true presence/absence of a loanword. Interestingly, Bayesian networks learned by our heuristic spanning tree based algorithm provide better results than the BIC optimal Bayesian networks.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

60203 - Linguistics

Projekt

<a href="/cs/project/GA20-18407S" target="_blank" >GA20-18407S: Automatizace analýzy slovesných tříd pro ohrožené jazyky - RoboCorp</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 12th Workshop on Uncertainty Processing (WUPES’22) Kutná Hora, Czech Republic

ISBN

978-80-7378-460-7

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

135-146

Název nakladatele

MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University

Místo vydání

Praha

Místo konání akce

Kutná Hora

Datum konání akce

1. 6. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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