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TWEETDICT : Identification of Topically Related Twitter Hashtags

  • This paper presents the TWEETDICT system prototype, which uses co-occurrence and frequency distributions of Twitter hashtags to generate clusters of keywords that could be used for topic summarization/identification. They also contain mentions referring to the same entity, which is a valuable resource for coreference resolution. We provide a web interface to the co-occurrence counts where an interactive search through the dataset collected from Twitter can be started. Additionally, the used data is also made freely available.

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Metadaten
Author:Fabian Dreer, Eduard Saller, Patrick Elsässer, Desislava Zhekova
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-2954
ISBN:978-3-934105-47-8
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:IBK; Internet-basierte Kommunikation; Soziale Medien
GND Keyword:Computerunterstützte Kommunikation; Onlinecommunity
Source:Workshop Proceedings of the 12th KONVENS 2014
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0