Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Mining social semantics on the social web
Guest editors: Andreas Hotho, Robert Jäschke and Kristina Lerman
Article type: Research Article
Authors: Şeker, Gökhan Akına | Eryiğit, Gülşenb; *
Affiliations: [a] ITU Informatics Institute, Istanbul Technical University, Istanbul, 34469, Turkey. E-mail: sekerg@itu.edu.tr | [b] Department of Computer Engineering, Istanbul Technical University Istanbul, 34469, Turkey. E-mail: gulsen.cebiroglu@itu.edu.tr
Correspondence: [*] Corresponding author. E-mail: gulsen.cebiroglu@itu.edu.tr.
Note: [1] This article is a revised and extended version of a paper that was presented at COLING-2012 [40]. This research is supported in part by a TUBITAK 1001 grant (no: 112E276) and is part of the ICT COST Action IC1207.
Abstract: Named entity recognition (NER), which provides useful information for many high level NLP applications and semantic web technologies, is a well-studied topic for most of the languages and especially for English. However, the modelling of morphologically rich languages (MRLs) for the NER task is still an open research area. The studies for Turkish which is a strong representative of MRLs have fallen behind the well-studied languages for a long while. In recent years, Turkish NER intrigued researchers due to its scarce data resources and the unavailability of high-performing systems. Especially, the need to semantically enrich the textual data coming with user generated content initiated many studies in this field. This article presents a CRF-based NER system which successfully models the morphologically very rich nature of this language, its extensions to expand the covered named entity types, and also to process extra challenging user generated content coming with Web 2.0. The article introduces the re-annotation of the available datasets and a brand new dataset from Web 2.0. The introduced approach reveals an exact match F1 score of 92% on a dataset collected from Turkish news articles and ∼65% on different datasets collected from Web 2.0. The proposed model is believed to be easily applied to similar MRLs with relevant resources.
Keywords: Named entity recognition, Turkish, user generated content, CRF, web data
DOI: 10.3233/SW-170253
Journal: Semantic Web, vol. 8, no. 5, pp. 625-642, 2017
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl