Conference paper Open Access

StoryLens: A Multiple Views Corpus for Location and Event Detection

Braşoveanu, Adrian M. P.; Nixon, Lyndon J. B.; Weichselbraun, Albert


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Corpus</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Named Entity Linking</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Geosemantics</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Event detection</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Information Extraction</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Natural Language Processing</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Fake news</subfield>
  </datafield>
  <controlfield tag="005">20190115153416.0</controlfield>
  <controlfield tag="001">2534262</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">25-27 June 2018</subfield>
    <subfield code="g">WIMS'18</subfield>
    <subfield code="a">8th International Conference on Web Intelligence, Mining and Semantics</subfield>
    <subfield code="c">Novi Sad, Serbia</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">MODUL Technology GmbH Vienna, Austria</subfield>
    <subfield code="a">Nixon, Lyndon J. B.</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</subfield>
    <subfield code="a">Weichselbraun, Albert</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">381242</subfield>
    <subfield code="z">md5:4bd76ff497e82c7e3aa684df08e3ea64</subfield>
    <subfield code="u">https://zenodo.org/record/2534262/files/WIMS2018_Storylens_Brasoveanu.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="y">Conference website</subfield>
    <subfield code="u">https://wims2018.pmf.uns.ac.rs/</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2018-06-27</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-invid-h2020</subfield>
    <subfield code="o">oai:zenodo.org:2534262</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</subfield>
    <subfield code="a">Braşoveanu, Adrian M. P.</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">StoryLens: A Multiple Views Corpus for Location and Event Detection</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-invid-h2020</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">687786</subfield>
    <subfield code="a">In Video Veritas – Verification of Social Media Video Content for the News Industry</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;The news media landscape tends to focus on long-running narratives. Correctly processing new information, therefore, requires considering multiple lenses when analyzing media content. Traditionally it would have been considered sufficient to extract the topics&amp;nbsp;or entities contained in a text in order to classify it, but today it is important to also look at more sophisticated annotations related to fine grained geolocation, events, stories and the relations between them. In order to leverage such lenses we propose a new corpus that offers a diverse set of annotations over texts collected from multiple media sources. We also showcase the framework used for creating the corpus, as well as how the information from the various lenses can be used in order to support different use cases in the EU project InVID for verifying the veracity of online video.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.1145/3227609.3227674</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
</record>
21
13
views
downloads
Views 21
Downloads 13
Data volume 5.0 MB
Unique views 20
Unique downloads 11

Share

Cite as