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Design Challenges and Misconceptions in Neural Sequence Labeling

arXiv 1806.04470 ↗First posted 12 Jun 2018cs.CLcited by 28

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Abstract (arXiv)

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2018.

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Citing papers per year: 2021: 8; 2022: 10; 2023: 1; 2024: 3; 2025: 5; 2026: 1.

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  1. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  2. 2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
  3. 2021A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognitioncited by 33
  4. 2021LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Promptingcited by 27
  5. 2022Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learningcited by 24
  6. 2022MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Scorecited by 20
  7. 2022METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweetscited by 12
  8. 2023One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NERcited by 11
  9. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 9
  10. 2021SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluationcited by 8
  11. 2022TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tagscited by 8
  12. 2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLcited by 6
16 more
  1. 2024Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Modelcited by 5
  2. 2021TMR: Evaluating NER Recall on Tough Mentionscited by 4
  3. 2021Larger-Context Tagging: When and Why Does It Work?cited by 4
  4. 2025MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimationcited by 3
  5. 2021Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Unitscited by 2
  6. 2022YATO: Yet Another deep learning based Text analysis Open toolkitcited by 2
  7. 2022LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognitioncited by 1
  8. 2022Prompt-based Text Entailment for Low-Resource Named Entity Recognitioncited by 1
  9. 2024A Span-based Model for Extracting Overlapping PICO Entities from RCT Publicationscited by 1
  10. 2026Assessment of Generative Named Entity Recognition in the Era of Large Language Modelscited by 1
  11. 2022Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinesecited by 0
  12. 2024ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformerscited by 0
  13. 2025myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Taggingcited by 0
  14. 2025Towards Efficient Quantity Retrieval from Text:An Approach via Description Parsing and Weak Supervisioncited by 0
  15. 2025Weakly Supervised Medical Entity Extraction and Linking for Chief Complaintscited by 0
  16. 2025Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentationcited by 0