@inproceedings{boldsen-wahlberg-2021-survey,
title = "Survey and reproduction of computational approaches to dating of historical texts",
author = "Boldsen, Sidsel and
Wahlberg, Fredrik",
editor = "Dobnik, Simon and
{\O}vrelid, Lilja",
booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may # " 31--2 " # jun,
year = "2021",
address = "Reykjavik, Iceland (Online)",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
url = "https://aclanthology.org/2021.nodalida-main.15",
pages = "145--156",
abstract = "Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological clues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="boldsen-wahlberg-2021-survey">
<titleInfo>
<title>Survey and reproduction of computational approaches to dating of historical texts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sidsel</namePart>
<namePart type="family">Boldsen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fredrik</namePart>
<namePart type="family">Wahlberg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-may 31–2 jun</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Dobnik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lilja</namePart>
<namePart type="family">Øvrelid</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Linköping University Electronic Press, Sweden</publisher>
<place>
<placeTerm type="text">Reykjavik, Iceland (Online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological clues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today.</abstract>
<identifier type="citekey">boldsen-wahlberg-2021-survey</identifier>
<location>
<url>https://aclanthology.org/2021.nodalida-main.15</url>
</location>
<part>
<date>2021-may 31–2 jun</date>
<extent unit="page">
<start>145</start>
<end>156</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Survey and reproduction of computational approaches to dating of historical texts
%A Boldsen, Sidsel
%A Wahlberg, Fredrik
%Y Dobnik, Simon
%Y Øvrelid, Lilja
%S Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2021
%8 may 31–2 jun
%I Linköping University Electronic Press, Sweden
%C Reykjavik, Iceland (Online)
%F boldsen-wahlberg-2021-survey
%X Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological clues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today.
%U https://aclanthology.org/2021.nodalida-main.15
%P 145-156
Markdown (Informal)
[Survey and reproduction of computational approaches to dating of historical texts](https://aclanthology.org/2021.nodalida-main.15) (Boldsen & Wahlberg, NoDaLiDa 2021)
ACL