Summary of the paper
| Title | HFST-SweNER ― A New NER Resource for Swedish |
|---|---|
| Authors | Dimitrios Kokkinakis, Jyrki Niemi, Sam Hardwick, Krister Lindén and 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). |
| Topics | Tools, Systems, Applications, LR Infrastructures and Architectures |
Full paper  |
HFST-SweNER ― A New NER Resource for Swedish |
| Bibtex | @InProceedings{KOKKINAKIS14.391, author = {Dimitrios Kokkinakis and Jyrki Niemi and Sam Hardwick and Krister Lindén and Lars Borin}, title = {HFST-SweNER ― A New NER Resource for Swedish}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |