| 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 | 557 | 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 | 321 | 2019 |
| Medical Text Classification Using Convolutional Neural Networks M Hughes, I Li, S Kotoulas Informatics for Health, 2017 | 238 | 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 | 157 | 2019 |
| 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 | 63 | 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 | 60 | 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 | 36 | 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 | 36 | 2018 |
| 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 | 20 | 2022 |
| 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 | 18 | 2019 |
| R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning I Li, A Fabbri, S Hingmire, D Radev COLING 2020, 2020 | 15 | 2020 |
| Global data science project for covid-19 summary report D Garcia-Gasulla, SA Napagao, I Li, H Maruyama, H Kanezashi, ... | 6 | 2020 |
| Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders I Li, V Yan, T Li, R Qu, D Radev ACL 2021, 2021 | 5 | 2021 |
| Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting I Li, P Sen, H Zhu, Y Li, D Radev Association for Computational Linguistics, Proceedings of the 6th Workshop …, 2021 | 3 | 2021 |
| Detecting bias in transfer learning approaches for text classification I Li arXiv preprint arXiv:2102.02114, 2021 | 2 | 2021 |
| 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 | 1 | 2022 |
| HiPool: Modeling Long Documents Using Graph Neural Networks I Li, A Feng, D Radev, R Ying arXiv preprint arXiv:2305.03319, 2023 | | 2023 |
| Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences A Feng, I Li, Y Jiang, R Ying arXiv preprint arXiv:2210.11794, 2022 | | 2022 |
| A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation I Li, T George, A Fabbri, T Liao, B Chen, R Kawamura, R Zhou, V Yan, ... arXiv preprint arXiv:2201.02312, 2022 | | 2022 |
| Neural Graph Transfer Learning in Natural Language Processing Tasks I Li Yale University, 2022 | | 2022 |
Dragomir RadevProfessor, Computer Science, Yale UniversityVerified email at yale.edu
