# FLERT: Document-Level Features for Named Entity Recognition

arXiv: https://arxiv.org/abs/2011.06993 · HTML: https://mlsanity.com/paper/2011.06993/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 30 of 45)

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- [ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition](https://mlsanity.com/paper/2112.06482/) (2021)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system](https://mlsanity.com/paper/2304.10893/) (2023)
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- [Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting](https://mlsanity.com/paper/2402.12801/) (2024)
- [CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset](https://mlsanity.com/paper/2310.16225/) (2023)
- [In the Name of Fairness: Assessing the Bias in Clinical Record De-identification](https://mlsanity.com/paper/2305.11348/) (2023)
- [hmBERT: Historical Multilingual Language Models for Named Entity Recognition](https://mlsanity.com/paper/2205.15575/) (2022)
- [CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain](https://mlsanity.com/paper/2112.08754/) (2021)
- [Examining risks of racial biases in NLP tools for child protective services](https://mlsanity.com/paper/2305.19409/) (2023)
- [Regularization for Long Named Entity Recognition](https://mlsanity.com/paper/2104.07249/) (2021)
- [Measuring Spurious Correlation in Classification: 'Clever Hans' in Translationese](https://mlsanity.com/paper/2308.13170/) (2023)
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- [Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents](https://mlsanity.com/paper/2206.10939/) (2022)
- [SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning](https://mlsanity.com/paper/2306.12552/) (2023)
- [Do "English" Named Entity Recognizers Work Well on Global Englishes?](https://mlsanity.com/paper/2404.13465/) (2024)
- [CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English](https://mlsanity.com/paper/2405.11865/) (2024)
- [Annotations for Exploring Food Tweets From Multiple Aspects](https://mlsanity.com/paper/2412.06179/) (2024)
- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)
- [Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures](https://mlsanity.com/paper/2211.02415/) (2022)
- [Enriching Relation Extraction with OpenIE](https://mlsanity.com/paper/2212.09376/) (2022)
- [Comparing and combining some popular NER approaches on Biomedical tasks](https://mlsanity.com/paper/2305.19120/) (2023)
- [An In-Context Schema Understanding Method for Knowledge Base Question Answering](https://mlsanity.com/paper/2310.14174/) (2023)
- [Assisted Knowledge Graph Authoring: Human-Supervised Knowledge Graph Construction from Natural Language](https://mlsanity.com/paper/2401.07683/) (2024)

## Abstract (arXiv)

Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features. In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature, namely "fine-tuning" and "feature-based LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality. We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.