| Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task T Yu, R Zhang, K Yang, M Yasunaga, D Wang, Z Li, J Ma, I Li, Q Yao, ... arXiv preprint arXiv:1809.08887, 2018 | 1029 | 2018 |
| Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model AR Fabbri, I Li, T She, S Li, DR Radev ACL 2019, 2019 | 533 | 2019 |
| Medical Text Classification Using Convolutional Neural Networks M Hughes, I Li, S Kotoulas Informatics for Health, 2017 | 294 | 2017 |
| ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks M Yasunaga, K Jungo, Z Rui, F Alexander, L Irene, D Friedman, ... AAAI 2019, 2019 | 221 | 2019 |
| Neural natural language processing for unstructured data in electronic health records: a review I Li, J Pan, J Goldwasser, N Verma, WP Wong, MY Nuzumlalı, B Rosand, ... Computer Science Review 46, 100511, 2022 | 133 | 2022 |
| Sparc: Cross-domain semantic parsing in context T Yu, R Zhang, M Yasunaga, YC Tan, XV Lin, S Li, H Er, I Li, B Pang, ... arXiv preprint arXiv:1906.02285, 2019 | 109 | 2019 |
| What are we depressed about when we talk about covid19: Mental health analysis on tweets using natural language processing I Li, Y Li, T Li, S Alvarez-Napagao, G Garcia-Gasulla, T Suzumura Fortieth SGAI International Conference on Artificial Intelligence 2020, 2020 | 73 | 2020 |
| What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning I Li, A Fabbri, R Tung, D Radev AAAI 2019, 2019 | 60 | 2019 |
| Tutorialbank: A manually-collected corpus for prerequisite chains, survey extraction and resource recommendation AR Fabbri, I Li, P Trairatvorakul, Y He, WT Ting, R Tung, C Westerfield, ... ACL 2018, 2018 | 51 | 2018 |
| A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation I Li, M Yasunaga, MY Nuzumlalı, C Caraballo, S Mahajan, H Krumholz, ... Machine Learning for Health Workshop, NeurIPS 2019, 2019 | 31 | 2019 |
| R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning I Li, A Fabbri, S Hingmire, D Radev COLING 2020, 2020 | 30 | 2020 |
| Kg-rank: Enhancing large language models for medical qa with knowledge graphs and ranking techniques R Yang, H Liu, Q Zeng, YH Ke, W Li, L Cheng, Q Chen, J Caverlee, ... arXiv preprint arXiv:2403.05881, 2024 | 15* | 2024 |
| Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations B Yang, D Liu, T Suzumura, R Dong, I Li In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys '23), 2023 | 15 | 2023 |
| Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders I Li, V Yan, T Li, R Qu, D Radev ACL 2021, 2021 | 14 | 2021 |
| Diffuser: efficient transformers with multi-hop attention diffusion for long sequences A Feng, I Li, Y Jiang, R Ying Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 12772 …, 2023 | 9 | 2023 |
| Evaluating large language models on wikipedia-style survey generation F Gao, H Jiang, R Yang, Q Zeng, J Lu, M Blum, T She, Y Jiang, I Li Findings of the Association for Computational Linguistics ACL 2024, 5405-5418, 2024 | 7* | 2024 |
| RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models D Liu, B Yang, H Du, D Greene, N Hurley, A Lawlor, R Dong, I Li | 7* | 2024 |
| Variational graph autoencoding as cheap supervision for AMR coreference resolution I Li, L Song, K Xu, D Yu Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 7 | 2022 |
| Global data science project for covid-19 summary report D Garcia-Gasulla, SA Napagao, I Li, H Maruyama, H Kanezashi, ... | 7 | 2020 |
| Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation R Yang, Q Zeng, K You, Y Qiao, L Huang, CC Hsieh, B Rosand, ... arXiv e-prints, arXiv: 2311.16588, 2023 | 5* | 2023 |
Dragomir RadevProfessor, Computer Science, Yale UniversityVerified email at yale.edu
