@inproceedings{wohlgenannt-etal-2016-extracting,
title = "Extracting Social Networks from Literary Text with Word Embedding Tools",
author = "Wohlgenannt, Gerhard and
Chernyak, Ekaterina and
Ilvovsky, Dmitry",
editor = "Hinrichs, Erhard and
Hinrichs, Marie and
Trippel, Thorsten",
booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4004",
pages = "18--25",
abstract = "In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wohlgenannt-etal-2016-extracting">
<titleInfo>
<title>Extracting Social Networks from Literary Text with Word Embedding Tools</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gerhard</namePart>
<namePart type="family">Wohlgenannt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Chernyak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dmitry</namePart>
<namePart type="family">Ilvovsky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2016-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Erhard</namePart>
<namePart type="family">Hinrichs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marie</namePart>
<namePart type="family">Hinrichs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thorsten</namePart>
<namePart type="family">Trippel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>The COLING 2016 Organizing Committee</publisher>
<place>
<placeTerm type="text">Osaka, Japan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text.</abstract>
<identifier type="citekey">wohlgenannt-etal-2016-extracting</identifier>
<location>
<url>https://aclanthology.org/W16-4004</url>
</location>
<part>
<date>2016-12</date>
<extent unit="page">
<start>18</start>
<end>25</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Extracting Social Networks from Literary Text with Word Embedding Tools
%A Wohlgenannt, Gerhard
%A Chernyak, Ekaterina
%A Ilvovsky, Dmitry
%Y Hinrichs, Erhard
%Y Hinrichs, Marie
%Y Trippel, Thorsten
%S Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wohlgenannt-etal-2016-extracting
%X In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text.
%U https://aclanthology.org/W16-4004
%P 18-25
Markdown (Informal)
[Extracting Social Networks from Literary Text with Word Embedding Tools](https://aclanthology.org/W16-4004) (Wohlgenannt et al., LT4DH 2016)
ACL