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                <title>Reference corpus of historical Slovene goo300k 1.2</title>
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              <publisher>Jožef Stefan Institute</publisher>
              <publicationYear>2015</publicationYear>
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                <description descriptionType="Abstract">goo300k is a manually annotated reference corpus of historical
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                  &#xd;
                  Each text contains extensive meta-data and per-page links to facsimiles, while the word tokens in the
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                <title>Digital library and corpus of historical Slovene IMP 1.1</title>
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                <title>MULTEXT-East "1984" annotated corpus 4.0</title>
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              <publisher>Jožef Stefan Institute</publisher>
              <publicationYear>2010</publicationYear>
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                <description descriptionType="Abstract">The novel "1984" by George Orwell is the central component of
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                  lemma and its MULTEXT(-East) morphosyntactic description (MSD, i.e., a fine-grained feature-structure
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              <titles>
                <title>Lexicon of historical Slovene imp25k 1.1</title>
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              <publisher>Jožef Stefan Institute</publisher>
              <publicationYear>2014</publicationYear>
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                <description descriptionType="Abstract">The imp25k lexicon of historical Slovene was created
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                <title>MULTEXT-East "1984" document corpus 4.0</title>
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              <publisher>Jožef Stefan Institute</publisher>
              <publicationYear>2010</publicationYear>
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                  &#xd;
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                  contain names and sentences.&#xd;
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                <title>MULTEXT-East non-commercial lexicons 4.0</title>
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              <publicationYear>2010</publicationYear>
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              <publicationYear>2016</publicationYear>
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              <publicationYear>2016</publicationYear>
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              <publicationYear>2016</publicationYear>
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                  the 4th Conference on CMC and Social Media Corpora for the Humanities. 27–28 September 2016,
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                <creator>
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              <titles>
                <title>Speech Database of Spoken Flight Information Enquiries SOFES 1.0</title>
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              <publisher>Faculty of Electrical Engineering, University of Ljubljana</publisher>
              <publicationYear>2017</publicationYear>
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                  <contributorName>European Commission</contributorName>
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              <dates>
                <date dateType="Issued">2017-06-17</date>
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                <date dateType="Available">2017-06-21T12:43:30Z</date>
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              </creators>
              <titles>
                <title>Brexit stance annotated tweets</title>
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              <publisher>Jožef Stefan Institute</publisher>
              <publicationYear>2016</publicationYear>
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                  <contributorName>European Commission</contributorName>
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              <dates>
                <date dateType="Issued">2016</date>
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                <date dateType="Available">2017-07-12T09:57:01Z</date>
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                  the EU. In the weeks before the referendum, starting on May 12, the UK geo-located Brexit-related
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                  labeled for the stance of their authors regarding Brexit: Positive (supporting Brexit), Negative
                  (opposing Brexit), or Neutral (uncommitted). The labeled tweets were used to train a classifier which
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                  &#xd;
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                  Miha Grčar, Darko Cherepnalkoski, Igor Mozetič, Petra Kralj Novak:&#xd;
                  Stance and influence of Twitter users regarding the Brexit referendum.&#xd;
                  Computational Social Networks 4/6. 2017. http://dx.doi.org/10.1186/s40649-017-0042-6
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              <publicationYear>2018</publicationYear>
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                  to generate social and environmental impact alongside a financial return. The tweets relevant to
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                  Twitter Search API, and annotated for sentiment labels "Negative", "Neutral" or "Positive" by a
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                  &#xd;
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                <creator>
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                <title>Multilingual Culture-Independent Word Analogy Datasets</title>
              </titles>
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                  and then partially translated and partially done from scratch for the other languages (Croatian,
                  Finnish, Estonian, Swedish, Latvian, Lithuanian, Russian and English).&#xd;
                  &#xd;
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                  &#xd;
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              <titles>
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              </titles>
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                data &amp; tools1814Slovenian language resource repository
                CLARIN.SIrepo-help@clarin.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
              </resourceType>
              <rightsList>
                <rights rightsURI="info:eu-repo/semantics/openAccess"/>
                <rights rightsURI="http://opensource.org/licenses/GPL-3.0"/>
              </rightsList>
              <descriptions>
                <description descriptionType="Abstract">ELMo language model (https://github.com/allenai/bilm-tf) used to
                  produce contextual word embeddings, trained on large monolingual corpora for 7 languages: Slovenian,
                  Croatian, Finnish, Estonian, Latvian, Lithuanian and Swedish. &#xd;
                  Each language's model was trained for approximately 10 epochs. Corpora sizes used in training range
                  from over 270 M tokens in Latvian to almost 2 B tokens in Croatian. About 1 million most common tokens
                  were provided as vocabulary during the training for each language model. The model can also infer OOV
                  words, since the neural network input is on the character level.&#xd;
                  &#xd;
                  Each model is in its own .tar.gz archive, consisting of two files: pytorch weights (.hdf5) and options
                  (.json). Both are needed for model inference, using allennlp
                  (https://github.com/allenai/allennlp/blob/master/tutorials/how_to/elmo.md) python library.
                </description>
              </descriptions>
            </resource>
          </payload>
        </oai_datacite>
      </metadata>
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  </ListRecords>
</OAI-PMH>
