Pre-trained POS tagging models for German social media (doi:10.11588/data/W3JBV4)

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Document Description

Citation

Title:

Pre-trained POS tagging models for German social media

Identification Number:

doi:10.11588/data/W3JBV4

Distributor:

heiDATA

Date of Distribution:

2020-03-26

Version:

1

Bibliographic Citation:

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1

Study Description

Citation

Title:

Pre-trained POS tagging models for German social media

Identification Number:

doi:10.11588/data/W3JBV4

Authoring Entity:

Rehbein, Ines (Leibniz Institute for the German Language)

Ruppenhofer, Josef (Leibniz Institute for the German Language)

Zimmermann, Victor (Department of Computational Linguistics, Heidelberg University)

Date of Production:

2018

Distributor:

heiDATA

Access Authority:

Rehbein, Ines

Date of Deposit:

2019-07-31

Holdings Information:

https://doi.org/10.11588/data/W3JBV4

Study Scope

Keywords:

Arts and Humanities, Computer and Information Science, Tweets, Twitter, POS tagging, Twitter POS testsuite, Social media data, Corpus, German

Topic Classification:

POS tagging models for German social media text, POS tagging

Abstract:

<p>Pre-trained POS tagging models for</p> <ol style="list-style-type: lower-alpha;"> <li>the HunPos tagger (Hal&aacute;csy et al. 2007)</li> <li>the biLSTM-char-CRF tagger (Reimers &amp; Gurevych 2017)</li> <li>Online-Flors (Yin et al. 2015).</li> </ol> <p><strong>References:</strong></p> <p>Hal&aacute;csy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In <em>Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions</em>, ACL&rsquo;07, pages 209&ndash;212, Prague, Czech Republic.</p> <p>Reimers, N., and Gurevych, I. (2017). Reportingscore distributions makes a difference: Performancestudy of lstm-networks for sequence tagging. In <span style="left: 171.017px; top: 1330.24px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.931795);">Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</span>, EMNLP, pp. 338&ndash;348, <span style="left: 200.077px; top: 1344.41px; font-size: 14.944px; font-family: sans-serif; transform: scaleX(0.915778);">September 7&ndash;11, 2017, Copenhagen, Denmark.</span></p> <p>Yin, W., Schnabel, T. and Sch&uuml;tze, H. (2015). Online updating of word representations forpart-of-speech tagging. In <em>Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing</em>, EMNLP&rsquo;15, pages 1329&ndash;1334. September 17-21, 2015, Lisbon, Portugal.</p>

Kind of Data:

Archived binary files

Kind of Data:

readme files

Kind of Data:

example files

Methodology and Processing

Sources Statement

Data Access

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Related Materials

<p>A harmonised testsuite for social media POS tagging (DE)<br /><a href="https://doi.org/10.11588/data/KXLMHN">https://doi.org/10.11588/data/KXLMHN</a> </p>

Related Publications

Citation

Title:

<p>Rehbein, I., Ruppenhofer, J., and Zimmermann, V. (2018). A harmonised testsuite for pos tagging of German social media data. In <em>Proceedings of the Conference on Natural Language Processing</em>, KONVENS 2018, pages 18&ndash;28, September 19-21, 2018, Vienna, Austria.</p>

Identification Number:

https://www.oeaw.ac.at/fileadmin/subsites/academiaecorpora/PDF/konvens18_03.pdf

Bibliographic Citation:

<p>Rehbein, I., Ruppenhofer, J., and Zimmermann, V. (2018). A harmonised testsuite for pos tagging of German social media data. In <em>Proceedings of the Conference on Natural Language Processing</em>, KONVENS 2018, pages 18&ndash;28, September 19-21, 2018, Vienna, Austria.</p>

Other Study-Related Materials

Label:

example.txt

Notes:

text/plain

Other Study-Related Materials

Label:

hunpos-social-media.model.bz2

Notes:

application/x-bzip

Other Study-Related Materials

Label:

output.txt

Notes:

text/plain

Other Study-Related Materials

Label:

readme-pretrained.txt

Notes:

text/plain