Graph clustering for natural language processing


Ustalov, Dmitry


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DOI: https://doi.org/10.5281/zenodo.1161505
URL: https://ub-madoc.bib.uni-mannheim.de/46524
URN: urn:nbn:de:bsz:180-madoc-465240
Document Type: Conference presentation
Year of publication: 2018
Conference title: Artificial Intelligence and Natural Language, 7th International Conference, AINL 2018
Location of the conference venue: St Petersburg, Russia
Date of the conference: October 17-19, 2018
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
Subject: 004 Computer science, internet
310 Statistics
Abstract: Graph-based representations are proven to be an effective approach for a variety of Natural Language Processing (NLP) tasks. Graph clustering makes it possible to extract useful knowledge by exploiting the implicit structure of the data. In this tutorial, we will present several efficient graph clustering algorithms, show their strengths and weaknesses as well as their implementations and applications. Then, the evaluation methodology in unsupervised NLP tasks will be discussed.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




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Ustalov, Dmitry ORCID: 0000-0002-9979-2188 Graph clustering for natural language processing. Open Access (2018) Artificial Intelligence and Natural Language, 7th International Conference, AINL 2018 (St Petersburg, Russia) [Conference presentation]
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