<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bogel-etal-2014-computational">
    <titleInfo>
        <title>Computational Narratology: Extracting Tense Clusters from Narrative Texts</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thomas</namePart>
        <namePart type="family">Bögel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jannik</namePart>
        <namePart type="family">Strötgen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Gertz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014-may</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association (ELRA)</publisher>
            <place>
                <placeTerm type="text">Reykjavik, Iceland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Computational Narratology is an emerging field within the Digital Humanities. In this paper, we tackle the problem of extracting temporal information as a basis for event extraction and ordering, as well as further investigations of complex phenomena in narrative texts. While most existing systems focus on news texts and extract explicit temporal information exclusively, we show that this approach is not feasible for narratives. Based on tense information of verbs, we define temporal clusters as an annotation task and validate the annotation schema by showing that the task can be performed with high inter-annotator agreement. To alleviate and reduce the manual annotation effort, we propose a rule-based approach to robustly extract temporal clusters using a multi-layered and dynamic NLP pipeline that combines off-the-shelf components in a heuristic setting. Comparing our results against human judgements, our system is capable of predicting the tense of verbs and sentences with very high reliability: for the most prevalent tense in our corpus, more than 95% of all verbs are annotated correctly.</abstract>
    <identifier type="citekey">bogel-etal-2014-computational</identifier>
    <location>
        <url>http://www.lrec-conf.org/proceedings/lrec2014/pdf/280_Paper.pdf</url>
    </location>
    <part>
        <date>2014-may</date>
        <extent unit="page">
            <start>950</start>
            <end>955</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Computational Narratology: Extracting Tense Clusters from Narrative Texts
%A Bögel, Thomas
%A Strötgen, Jannik
%A Gertz, Michael
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F bogel-etal-2014-computational
%X Computational Narratology is an emerging field within the Digital Humanities. In this paper, we tackle the problem of extracting temporal information as a basis for event extraction and ordering, as well as further investigations of complex phenomena in narrative texts. While most existing systems focus on news texts and extract explicit temporal information exclusively, we show that this approach is not feasible for narratives. Based on tense information of verbs, we define temporal clusters as an annotation task and validate the annotation schema by showing that the task can be performed with high inter-annotator agreement. To alleviate and reduce the manual annotation effort, we propose a rule-based approach to robustly extract temporal clusters using a multi-layered and dynamic NLP pipeline that combines off-the-shelf components in a heuristic setting. Comparing our results against human judgements, our system is capable of predicting the tense of verbs and sentences with very high reliability: for the most prevalent tense in our corpus, more than 95% of all verbs are annotated correctly.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/280_Paper.pdf
%P 950-955

Markdown (Informal)

[Computational Narratology: Extracting Tense Clusters from Narrative Texts](http://www.lrec-conf.org/proceedings/lrec2014/pdf/280_Paper.pdf) (Bögel et al., LREC 2014)

• Computational Narratology: Extracting Tense Clusters from Narrative Texts (Bögel et al., LREC 2014)

ACL

• Thomas Bögel, Jannik Strötgen, and Michael Gertz. 2014. Computational Narratology: Extracting Tense Clusters from Narrative Texts. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 950–955, Reykjavik, Iceland. European Language Resources Association (ELRA).