# Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection

arXiv: https://arxiv.org/abs/1707.02483 · HTML: https://mlsanity.com/paper/1707.02483/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2017.

## Built on by (most cited first, 30 of 36)

- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling](https://mlsanity.com/paper/2103.07792/) (2021)
- [Dataset Geography: Mapping Language Data to Language Users](https://mlsanity.com/paper/2112.03497/) (2021)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER](https://mlsanity.com/paper/2106.02300/) (2021)
- [Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data](https://mlsanity.com/paper/2109.04319/) (2021)
- [CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation](https://mlsanity.com/paper/2210.07022/) (2022)
- [XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment](https://mlsanity.com/paper/2104.08597/) (2021)
- [Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2106.00241/) (2021)
- [T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks](https://mlsanity.com/paper/2212.10548/) (2022)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning](https://mlsanity.com/paper/2110.04429/) (2021)
- [Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment](https://mlsanity.com/paper/2101.11112/) (2021)
- [Contextual Label Projection for Cross-Lingual Structured Prediction](https://mlsanity.com/paper/2309.08943/) (2023)
- [Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages](https://mlsanity.com/paper/2201.12219/) (2022)
- [Multilingual Sentence Transformer as A Multilingual Word Aligner](https://mlsanity.com/paper/2301.12140/) (2023)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [Constrained Decoding for Cross-lingual Label Projection](https://mlsanity.com/paper/2402.03131/) (2024)
- [WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2212.03506/) (2022)
- [X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing](https://mlsanity.com/paper/2106.03777/) (2021)
- [Cross-Lingual Text Classification of Transliterated Hindi and Malayalam](https://mlsanity.com/paper/2108.13620/) (2021)
- [A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2101.00388/) (2021)
- [A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2204.00796/) (2022)
- [ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2301.08855/) (2023)
- [CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2305.14913/) (2023)
- [Few Dimensions are Enough: Fine-tuning BERT with Selected Dimensions Revealed Its Redundant Nature](https://mlsanity.com/paper/2504.04966/) (2025)
- [AdaK-NER: An Adaptive Top-K Approach for Named Entity Recognition with Incomplete Annotations](https://mlsanity.com/paper/2109.05233/) (2021)
- [Building and Evaluating Universal Named-Entity Recognition English corpus](https://mlsanity.com/paper/2212.07162/) (2022)

## Abstract (arXiv)

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.