# POLYGLOT-NER: Massive Multilingual Named Entity Recognition

arXiv: https://arxiv.org/abs/1410.3791 · HTML: https://mlsanity.com/paper/1410.3791/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2014.

## Built on by (most cited first, 30 of 32)

- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [PyThaiNLP: Thai Natural Language Processing in Python](https://mlsanity.com/paper/2312.04649/) (2023)
- [Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition](https://mlsanity.com/paper/2107.11610/) (2021)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Rethinking Negative Instances for Generative Named Entity Recognition](https://mlsanity.com/paper/2402.16602/) (2024)
- [Incorporating Context into Subword Vocabularies](https://mlsanity.com/paper/2210.07095/) (2022)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages](https://mlsanity.com/paper/2201.12219/) (2022)
- [Resources and Few-shot Learners for In-context Learning in Slavic Languages](https://mlsanity.com/paper/2304.01922/) (2023)
- [JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning](https://mlsanity.com/paper/2310.02953/) (2023)
- [Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies](https://mlsanity.com/paper/2402.09282/) (2024)
- [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://mlsanity.com/paper/2406.11192/) (2024)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios](https://mlsanity.com/paper/2209.02139/) (2022)
- [RUIE: Retrieval-based Unified Information Extraction using Large Language Model](https://mlsanity.com/paper/2409.11673/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [TNNT: The Named Entity Recognition Toolkit](https://mlsanity.com/paper/2108.13700/) (2021)
- [D-HAN: Dynamic News Recommendation with Hierarchical Attention Network](https://mlsanity.com/paper/2112.10085/) (2021)
- [Improving the Quality of Neural Machine Translation Through Proper Translation of Name Entities](https://mlsanity.com/paper/2305.07360/) (2023)
- [Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2309.00246/) (2023)
- [Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases](https://mlsanity.com/paper/2403.16396/) (2024)
- [New Textual Corpora for Serbian Language Modeling](https://mlsanity.com/paper/2405.09250/) (2024)
- [Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset](https://mlsanity.com/paper/2601.00411/) (2026)
- [Dutch Named Entity Recognition and De-identification Methods for the Human Resource Domain](https://mlsanity.com/paper/2106.02287/) (2021)
- [MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages](https://mlsanity.com/paper/2212.09723/) (2022)
- [Machine Translation for Accessible Multi-Language Text Analysis](https://mlsanity.com/paper/2301.08416/) (2023)
- [WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition](https://mlsanity.com/paper/2304.04026/) (2023)
- [Exploring the Use of Foundation Models for Named Entity Recognition and Lemmatization Tasks in Slavic Languages](https://mlsanity.com/paper/2304.05336/) (2023)

## Abstract (arXiv)

The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.