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Towards High Accuracy Named Entity Recognition for Icelandic

Svanhvít Ingólfsdóttir
Department of Computer Science, Reykjavik University, Iceland

Sigurjó Þorsteinsson
Department of Computer Science, Reykjavik University, Iceland

Hrafn Loftsson
Department of Computer Science, Reykjavik University, Iceland

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Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Linköping Electronic Conference Proceedings 167:42, s. 363--369

NEALT Proceedings Series 42:42, s. 363--369

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Publicerad: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

We report on work in progress which consists of annotating an Icelandic corpus for named entities (NEs) and using it for training a named entity recognizer based on a Bidirectional Long Short-Term Memory model. Currently, we have annotated 7,538 NEs appearing in the first 200,000 tokens of a 1 million token corpus, MIMGOLD, originally developed for serving as a gold standard for part-of-speech tagging. Our best performing model, trained on this subset of MIM-GOLD, and enriched with external word embeddings, obtains an overall F1 score of 81.3% when categorizing NEs into the following four categories: persons, locations, organizations and miscellaneous. Our preliminary results are promising, especially given the fact that 80% of MIM-GOLD has not yet been used for training.

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