HFST-SweNER – A New NER Resource for Swedish

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http://hdl.handle.net/10138/136265

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Kokkinakis , D , Niemi , J , Hardwick , S , Linden , K & Borin , L 2014 , HFST-SweNER – A New NER Resource for Swedish . in N Calzolari , K Choukri , T Declerck , H Loftsson , B Maegaard , J Mariani , A Moreno , J Odijk & S Piperidis (eds) , Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) . , #391 , European Language Resources Association (ELRA) , Reykjavik, Iceland , Language and Resource Evaluation Conference , Reykjavik , Iceland , 26/05/2014 .

Title: HFST-SweNER – A New NER Resource for Swedish
Author: Kokkinakis, Dimitrios; Niemi, Jyrki; Hardwick, Sam; Linden, Krister; Borin, Lars
Other contributor: Calzolari, Nicoletta
Choukri, Khalid
Declerck, Thierry
Loftsson, Hrafn
Maegaard, Bente
Mariani, Joseph
Moreno, Asuncion
Odijk, Jan
Piperidis, Stelios
Contributor organization: Department of Modern Languages 2010-2017
Krister Linden / Research Group
Publisher: European Language Resources Association (ELRA)
Date: 2014-05-26
Language: eng
Number of pages: 7
Belongs to series: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
ISBN: 978-2-9517408-8-4
978-2-9517408-8-4
URI: http://hdl.handle.net/10138/136265
Abstract: Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).
Subject: 113 Computer and information sciences
finite-state transducers
6121 Languages
named entity recognition
Swedish
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: publishedVersion


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