Tagging named entities in Croatian tweets

Baksa, Krešimir ; Golović, Dino ; Glavaš, Goran ; Šnajder, Jan

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DOI:	https://doi.org/10.4312/slo2.0.2016.1.20-41
URL:	https://madoc.bib.uni-mannheim.de/60356
Additional URL:	https://revije.ff.uni-lj.si/slovenscina2/article/v...
URN:	urn:nbn:de:bsz:180-madoc-603568
Document Type:	Article
Year of publication:	2016
The title of a journal, publication series:	Slovenščina 2.0
Volume:	4
Issue number:	1
Page range:	20-41
Place of publication:	Ljubljana
Publishing house:	Ljubljana University Press, Faculty of Arts
ISSN:	2335-2736
Related URLs:	https://www.researchgate.net/publication... https://www.semanticscholar.org/paper/Ta...
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Text Analytics for Interdisciplinary Research (Juniorprofessur) (Glavaš 2017-2021)
Pre-existing license:	Creative Commons Attribution, Share Alike 4.0 International (CC BY-SA 4.0)
Subject:	004 Computer science, internet
Abstract:	Named entity extraction tools designed for recognizing named entities in texts written in standard language (e.g., news stories or legal texts) have been shown to be inadequate for user-generated textual content (e.g., tweets, forum posts). In this work, we propose a supervised approach to named entity recognition and classification for Croatian tweets. We compare two sequence labelling models: a hidden Markov model (HMM) and conditional random fields (CRF). Our experiments reveal that CRF is the best model for the task, achieving a very good performance of over 87% micro-averaged F1 score. We analyse the contributions of different feature groups and influence of the training set size on the performance of the CRF model.
Additional information:	Online-Ressource

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