---
title: "Cross-lingual Name Tagging and Linking for 282 — Pan 2017"
description: "The paper addresses name tagging and entity linking for languages with almost no annotated data."
authors: ["Xiaoman Pan", "Boliang Zhang", "Jonathan May", "Joel Nothman", "Kevin Knight", "Heng Ji"]
year: 2017
venue: "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P17-1178/"
doi: "10.18653/v1/p17-1178"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pan-2017-cross-lingual-name-tagging-linking/"
---
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# Cross-lingual Name Tagging and Linking for 282 Languages

**Authors:** [Xiaoman Pan](https://md.research.elamur.ai/free/en/authors/xiaoman-pan/), Boliang Zhang, [Jonathan May](https://md.research.elamur.ai/free/en/authors/jonathan-may/), [Joel Nothman](https://md.research.elamur.ai/free/en/authors/joel-nothman/), [Kevin Knight](https://md.research.elamur.ai/free/en/authors/kevin-knight/), [Heng Ji](https://md.research.elamur.ai/free/en/authors/heng-ji/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P17-1178/) · [PDF](https://aclanthology.org/P17-1178.pdf) · [DOI 10.18653/v1/p17-1178](https://doi.org/10.18653/v1/p17-1178) · [OpenAlex](https://openalex.org/W2742113707)

**Citations:** 312 citations (openalex-snapshot · 2026-10-14) · [356](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses name tagging and entity linking for languages with almost no annotated data. It aims to cover all 282 languages present in Wikipedia and to link mentions to an English KB (English Wikipedia). Instead of relying on manual labels, the authors build "silver-standard" training data automatically:

- **Typing English pages.** English Wikipedia pages are assigned types by Maximum Entropy classifiers. These use DBpedia properties as features and are trained on mentions from the AMR corpus.
- **Spreading labels.** The types are carried to other languages through cross-lingual and redirect links, then projected onto anchor links.
- **Refining labels.** Self-training with NPMI-filtered seeds fills in missing labels. For large languages, sentences are filtered by entity commonness and topic relatedness.
- **Morphology.** Wikipedia bracket markup provides affix lists for morphologically rich languages.
- **Linking.** Aligned cross-lingual titles yield word translations that help generate linking candidates.

A BiLSTM-CRF tagger is trained separately for each language. Results are promising on Wikipedia text and on external data such as the LORELEI sets, without any native-speaker input. All annotations, morphology resources and systems are released as a public benchmark (WikiANN), a common starting point for low-resource NER, including Ukrainian.

## Key points
- **Typing schemas.** Mentions get types at three granularities:
  - 4 coarse types (PER, ORG, GPE, LOC);
  - 139 AMR types;
  - 9,154 YAGO types, matched to AMR types via PMI.
- **English page classifier.** It uses 60,231 DBpedia properties as features and labels about 10 million English pages as entities. On the 4,853 pages hand-annotated by Nothman et al. (2013), it reaches 96.0% F-score, versus 94.6% in that earlier work.
- **Tagger and features.** The tagger is a BiLSTM-CRF (Lample et al., 2016) with these features:
  - word form, case and syllable features;
  - stems and affixes;
  - cross-lingual gazetteers;
  - character and word embeddings (character-only for languages without word segmentation).
- **Ablation results on LORELEI data.**
  - Markup-derived morphology features add 11.1 F-score points for Turkish and 7.1 for Uzbek.
  - Topic-based data selection raises Turkish from 49.7% to 51.5% while also shrinking the training set.
- **Linking approach.** Mined word translations let names be translated into English and then linked with an unsupervised English linker (Pan et al., 2015).

## Interesting ideas
- **Typing entities by their KB properties.** Properties such as a birth date or a population count serve as language-independent typing features. This avoids POS taggers or other per-language tools, so it could type entities in any KB-backed project.
- **Free morphology from link markup.** In `[[stem]]suffix` patterns, the text inside the brackets is the canonical form and whatever follows is an affix. This gives cheap stemming for agglutinative languages, and similar link markup in wikis or annotated corpora could be mined the same way.
- **Confidence-gated self-training.** Seed sentences are kept only if every tag–token pair passes an NPMI threshold. Unlabeled text is then grouped into clusters by cross-entropy and absorbed one cluster at a time. This is a useful recipe whenever distant supervision leaves labels sparse, such as linking only the first mention of a name.
- **Curating training data for the target domain.** Sentences are ranked by entity commonness and topic relatedness to the target domain. This made training faster and also improved quality, which is worth remembering when large silver corpora are noisy and time is limited.
- **Dictionaries from title alignment.** Wikipedia titles follow consistent styles within each language, so aligning cross-lingual title pairs yields word-level translation pairs. Such a dictionary covers names that never appear as whole title pairs, which is helpful for cross-lingual search or gazetteer building.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

356 papers of our library cite it (of the 140884 whose bibliographies we read); 29 of them have a page here, 327 not yet.

- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://md.research.elamur.ai/free/en/papers/adelani-2023-sib-200-simple-inclusive-big/)
- [Syntax-augmented Multilingual BERT for Cross-lingual Transfer](https://md.research.elamur.ai/free/en/papers/ahmad-2021-syntax-augmented-multilingual-bert-cross/)
- [MEGA: Multilingual Evaluation of Generative AI](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/)
- [Translation Artifacts in Cross-lingual Transfer Learning](https://md.research.elamur.ai/free/en/papers/artetxe-2020-translation-artifacts-cross-lingual-transfer/)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://md.research.elamur.ai/free/en/papers/bandarkar-2023-belebele-benchmark-parallel-reading-comprehension/)
- [Entity Linking in 100 Languages](https://md.research.elamur.ai/free/en/papers/botha-2020-entity-linking-100-languages/)
- [An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://md.research.elamur.ai/free/en/papers/burchell-2025-expanded-massive-multilingual-dataset-high/)
- [Rethinking embedding coupling in pre-trained language models](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/)
- [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
- [On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://md.research.elamur.ai/free/en/papers/hu-2020-xtreme-massively-multilingual-multi-task/)
- [IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/)
- [Parser combinators for Tigrinya and Oromo morphology](https://md.research.elamur.ai/free/en/papers/lingbulk-2018-littell-parser-combinators-tigrinya-oromo-morphology/)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/muller-2021-when-being-unseen-mbert-is/)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-mad-x-adapter-based-framework/)
- [UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://md.research.elamur.ai/free/en/papers/pfeiffer-2021-unks-everywhere-adapting-multilingual-language/)
- [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/)
- [Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset](https://md.research.elamur.ai/free/en/papers/plum-2026-do-llms-judge-distantly-supervised/)
- [Massively Multilingual Transfer for NER](https://md.research.elamur.ai/free/en/papers/rahimi-2019-massively-multilingual-transfer-ner/)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/)
- [How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/)
- [On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment](https://md.research.elamur.ai/free/en/papers/wang-2020-negative-interference-multilingual-models-findings/)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
- [Are All Languages Created Equal in Multilingual BERT?](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
- [Evaluating the Adaptability of Large Language Models to Linguistic Variation](https://md.research.elamur.ai/free/en/papers/xu-2026-evaluating-adaptability-large-language-models/)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pan2017cross,
  title = {{Cross-lingual Name Tagging and Linking for 282 Languages}},
  author = {Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-1178},
  url = {https://doi.org/10.18653/v1/p17-1178}
}
```

- [← previous Creating Training Corpora for NLG Micro-Planners](https://md.research.elamur.ai/free/en/papers/gardent-2017-creating-training-corpora-nlg-micro/)
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- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/)

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