bibtype	K - Conference Paper (Czech conference)
ARLID	0558164
utime	20230316105122.4
mtime	20220614235959.9
title (primary) (eng)	Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks
specification	page_count 12 s. media_type P
serial	ARLID cav_un_epca*0558134 ISBN 978-80-7378-460-7 title Proceedings of the 12th Workshop on Uncertainty Processing page_num 135-146 publisher place Prague name MatfyzPress year 2022 editor name1 Studený name2 Milan editor name1 Ay name2 Nihat editor name1 Coletti name2 Giulianella editor name1 Kleiter name2 Gernot D. editor name1 Shenoy name2 Prakash P.
keyword	loanwords
keyword	Bayesian methods
keyword	probabilistic graphical model
author (primary)	ARLID cav_un_auth*0414315 name1 Kratochvíl name2 F. country CZ
author	ARLID cav_un_auth*0216188 name1 Kratochvíl name2 Václav institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept Department of Decision Making Theory department (cz) MTR department MTR full_dept Department of Decision Making Theory country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0431676 name1 Saad name2 G. country CZ
author	ARLID cav_un_auth*0101228 name1 Vomlel name2 Jiří institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept Department of Decision Making Theory department (cz) MTR department MTR full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2022/MTR/kratochvil-0558164.pdf
cas_special	project project_id GA20-18407S agency GA ČR ARLID cav_un_auth*0397557 abstract (eng) 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, 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) modeling the diffusion of a special class of loanwords, so-called Wanderw ̈orter 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 geographical distances, the travel times of sailing ships should be considered as a major factor determining the intensity of contact among cultures. We use sailing navigation software to estimate travel times between different ports and show that the estimated travel times correspond well to the 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. action ARLID cav_un_auth*0431432 name WUPES 2022: 12th Workshop on Uncertainty Processing dates 20220601 mrcbC20-s 20220604 place Kutná Hora country CZ RIV BA FORD0 10000 FORD1 10100 FORD2 10102 reportyear 2023 presentation_type PR inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0332323 confidential S arlyear 2022 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU63 cav_un_epca*0558134 Proceedings of the 12th Workshop on Uncertainty Processing MatfyzPress 2022 Prague 135 146 978-80-7378-460-7 mrcbU67 Studený Milan 340 mrcbU67 Ay Nihat 340 mrcbU67 Coletti Giulianella 340 mrcbU67 Kleiter Gernot D. 340 mrcbU67 Shenoy Prakash P. 340