# Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features

arXiv: https://arxiv.org/abs/1701.09123 · HTML: https://mlsanity.com/paper/1701.09123/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective](https://mlsanity.com/paper/2204.04391/) (2022)
- [Social Analysis of Young Basque Speaking Communities in Twitter](https://mlsanity.com/paper/2109.03487/) (2021)
- [On the Role of Morphological Information for Contextual Lemmatization](https://mlsanity.com/paper/2302.00407/) (2023)
- [From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://mlsanity.com/paper/2305.04928/) (2023)
- [CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English](https://mlsanity.com/paper/2405.11865/) (2024)
- [Evaluating Shortest Edit Script Methods for Contextual Lemmatization](https://mlsanity.com/paper/2403.16968/) (2024)
- [Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data](https://mlsanity.com/paper/2510.07434/) (2025)

## Abstract (arXiv)

We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.