We present a corpus of Finnish news articles with a manually prepared named entity annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source. The corpus is available for research purposes. We present baseline experiments on the corpus using a rule-based and two deep learning systems on two, in-domain and out-of-domain, test sets.
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While there does exist a body of work aiming at learning named entity recognition systems from unannotated data in an unsupervised manner, supervised learning from annotated data has been the prevalent approach in literature.
The data sets are available through FIN-CLARIN at http://urn.fi/urn:nbn:fi:lb-2019050201.
The FiNER system and its technical documentation are available at http://urn.fi/urn:nbn:fi:lb-2018091301.
It should be noted that entities can, in general, overlap in two ways, namely, by being nested or by crossing. In the latter, two entities overlap but neither is contained in another. However, such cases were not encountered in the data.
Note that the GENIA annotation allowed the top-level and nested entities to be of the same class increasing the nested/all-ratio. We are not able to say if this is the case with NoSta-D based on (Benikova et al. 2014).
These are instances of named entities, not types. For example, New York may occur several times in the corpus. Each occurrence is considered a separate named entity.
https://dumps.wikimedia.org/fiwiki/latest/fiwiki-latest-pages-articles.xml.bz2 downloaded 1.2.2018.
The pretrained word embeddings can be obtained directly from http://bionlp-www.utu.fi/fin-vector-space-models/fin-word2vec.bin.
We employ the pretrained FinnTreeBank tagger available at https://github.com/mpsilfve/FinnPos.
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We would like to express our sincere gratitude to Onur Güngör and Mohammad Golam Sohrab for their invaluable help with running the experiments. This work was funded by Academy of Finland (Award Numbers 292260 and 293239) and FIN-CLARIN. The third author has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 771113).
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Ruokolainen, T., Kauppinen, P., Silfverberg, M. et al. A Finnish news corpus for named entity recognition. Lang Resources & Evaluation 54, 247–272 (2020). https://doi.org/10.1007/s10579-019-09471-7
- Named entity recognition