HFST-SweNER — A New NER Resource for Swedish
Dimitrios Kokkinakis, Jyrki Niemi, Sam Hardwick, Krister Lindén, Lars Borin
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).- Anthology ID:
- L14-1339
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2537–2543
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/391_Paper.pdf
- DOI:
- Cite (ACL):
- Dimitrios Kokkinakis, Jyrki Niemi, Sam Hardwick, Krister Lindén, and Lars Borin. 2014. HFST-SweNER — A New NER Resource for Swedish. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2537–2543, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- HFST-SweNER — A New NER Resource for Swedish (Kokkinakis et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/391_Paper.pdf