# A Finnish News Corpus for Named Entity Recognition

arXiv: https://arxiv.org/abs/1908.04212 · HTML: https://mlsanity.com/paper/1908.04212/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model](https://mlsanity.com/paper/2104.09617/) (2021)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)
- [The Current State of Finnish NLP](https://mlsanity.com/paper/2109.11326/) (2021)
- [Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://mlsanity.com/paper/2106.03389/) (2021)
- [Cross-lingual alignments of ELMo contextual embeddings](https://mlsanity.com/paper/2106.15986/) (2021)
- [Word Boundary Information Isn't Useful for Encoder Language Models](https://mlsanity.com/paper/2401.07923/) (2024)
- [Tokenization Strategies for Low-Resource Agglutinative Languages in Word2Vec: Case Study on Turkish and Finnish](https://mlsanity.com/paper/2509.14238/) (2025)
- [CodeNER: Code Prompting for Named Entity Recognition](https://mlsanity.com/paper/2507.20423/) (2025)
- [AWED-PIPER: Agents, Web Applications & Expert Detectors for Personally Identifiable Information Protection & Fine-grained Named Entity Recognition across 36 languages for 6.6 Billion Speakers](https://mlsanity.com/paper/2601.10161/) (2026)

## Abstract (arXiv)

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.