Text Chunking Using Transformation-Based Learning

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

Tjong Kim Sang, E. F., & De Meulder, F. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119195 · Cited by 2,788

Publicly Available Clinical

Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1909 · Cited by 1,736

Shallow parsing with conditional random fields

Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246

Sentiment Analysis Is a Big Suitcase

Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.4531228 · Cited by 439

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system

Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N., & Lazarus, R. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-30 · Cited by 360

Conditional random fields for activity recognition

Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T.-S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.254 · Cited by 193

Reducing Quantity Hallucinations in Abstractive Summarization

Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99

Argument Mining for Understanding Peer Reviews

Hua, X., Nikolov, M., Badugu, N., & Wang, L. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1219 · Cited by 86

A study of deep learning methods for de-identification of clinical notes in cross-institute settings

Yang, X., Lyu, T., Li, Q., Lee, C.-Y., Bian, J., Hogan, W. R., & Wu, Y. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0935-4 · Cited by 73

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Jain, S., Agrawal, A., Saporta, A., Truong, S. Q., Duong, D. N., Bui, T., Chambon, P., Zhang, Y., Lungren, M. P., Ng, A. Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.14463 · Cited by 66

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

Cheng, L., Bing, L., Yu, Q., Lu, W., & Si, L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.569 · Cited by 58

An intrinsic stopping criterion for committee-based active learning

Olsson, F., & Tomanek, K. (2009) · Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09 · doi:10.3115/1596374.1596398 · Cited by 37

A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation

Stojanov, R., Popovski, G., Cenikj, G., Koroušić Seljak, B., & Eftimov, T. (2021) · Journal of Medical Internet Research · doi:10.2196/28229 · Cited by 37

Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

Yang, X., Zhang, H., He, X., Bian, J., & Wu, Y. (2020) · JMIR Medical Informatics · doi:10.2196/22982 · Cited by 29

PICO entity extraction for preclinical animal literature.

Wang, Q., Liao, J., Lapata, M., & Macleod, M. (2022) · Systematic Reviews · doi:10.1186/s13643-022-02074-4 · Cited by 29

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.198 · Cited by 23

EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature.

Stylianou, N., Razis, G., Goulis, D. G., & Vlahavas, I. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101949 · Cited by 22

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews

Panayi, A., Ward, K., Benhadji-Schaff, A., Ibanez-Lopez, A. S., Xia, A., & Barzilay, R. (2023) · Systematic Reviews · doi:10.1186/s13643-023-02351-w · Cited by 23

GridBook: Natural Language Formulas for the Spreadsheet Grid

Srinivasa Ragavan, S., Hou, Z., Wang, Y., Gordon, A. D., Zhang, H., & Zhang, D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511161 · Cited by 15

Sequence-aware multimodal page classification of Brazilian legal documents

Luz de Araujo, P. H., de Almeida, A. P. G. S., Ataides Braz, F., Correia da Silva, N., de Barros Vidal, F., & de Campos, T. E. (2023) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-022-00406-7 · Cited by 9

Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials.

Witte, C., Schmidt, D. M., & Cimiano, P. (2024) · Journal of Biomedical Semantics · doi:10.1186/s13326-024-00305-2 · Cited by 7

Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines

Azab, M., Dadian, S., Nastase, V., An, L., & Mihalcea, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1122 · Cited by 5

ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference

Liu, X., Tang, Z., Dong, P., Li, Z., Liu, Y., Li, B., Hu, X., & Chu, X. (2025) · arXiv · doi:10.48550/arxiv.2502.00299 · Cited by 4