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HFST-SweNER . A New NER Resource for Swedish

Conference paper
Authors Dimitrios Kokkinakis
Jyrki Niemi
sam hardwick
Krister Lindén
Lars Borin
Published in Proceedings of the 9th edition of the Language Resources and Evaluation Conference (LREC), Reykjavik 26 - 31 May 2014.
Pages 2537-2543
ISBN 978-2-9517408-8-4
Publication year 2014
Published at Department of Swedish
Pages 2537-2543
Language en
Keywords named entity recognition, finite-state transducers
Subject categories Information technology, Language Technology (Computational Linguistics), Computational linguistics, Linguistics


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).

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