Hi there, I’m Li, Irene(李紫辉)! Welcome to my blog! 🙂
I want to share my learning journals, notes and programming exercises with you. The topics include data science, statistics, machine learning, deep learning, AI applications, etc.
I am a fourth-year Ph.D. student, working in LILY lab at Yale University. My advisor is Prof. Dragomir Radev. I joined the Center for Outcomes Research & Evaluation (CORE) from Yale School of Medicine in the year 2020.
Covid-19 Event 🦠 : I serve as a volunteer data scientist in the GDSP (Global Data Science Project), focusing on understanding secondary impacts on societal aspects. I am the sub-project PI for the Emotion Analysis Project.
Please contact me via irene.li[at]yale.edu.
Personal Links
Recent Research
- R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Swapnil Hingmire, and Dragomir Radev
COLING, 2020. Github - What are We Depressed about When We Talk about COVID-19: Mental Health Analysis on Tweets Using Natural Language Processing
Irene Li, Yixin Li, Tianxiao Li, Sergio Alvarez-Napagao, Dario Garcia-Gasulla, Toyotaro Suzumura
Fortieth SGAI International Conference on Artificial Intelligence, 2020 - A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlalı, Cesar Caraballo, Shiwani Mahajan, Harlan Krumholz, and Dragomir Radev
Machine Learning for Health Workshop at NeurIPS 2019, Code - Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev
Proceedings of ACL 2019, Dataset and Code - What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Irene Li, Alexander Fabbri, Robert Tung, and Dragomir Radev
Proceedings of AAAI 2019, Dataset BLOG - TutorialBank: Using a Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, and Dragomir Radev
Proceedings of ACL 2018 Dataset BLOG
Participated Research
- SParC: Cross-Domain Semantic Parsing in Context
Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi-Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher and Dragomir Radev
Proceedings of ACL 2019 - ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander Fabbri, Irene Li, Dan Friedman, and Dragomir Radev
Proceedings of AAAI 2019 - Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev
Proceedings of EMNLP 2018 BLOG
Earlier Research Works (Before my PhD)
Recent Posts
- Slideshare (11): Graph Neural Networks and Applications in NLP
- Slideshare (10): Reading notes bout Text Generation, BERT, Knowledge Distillation…
- Paddle Serving: model-as-a-service! Triggered by a single command line, deployment finishes in 10 minutes
- Slideshare (9): Transfer learning tutorial 2.0
- Slideshare (8): Graph Neural Network Paper reading notes
- Slideshare (7): NeurIPS 2019 Notes on NLP and GCNs
- Expanded AAN.how: Learning NLP Made Much Easier!
- Slideshare (6): Cross-lingual Paper reading notes
- Slideshare (5): Graph Neural Networks
- Prepare for the Interviews!
- Deep Learning 18: GANs with PyTorch
- Deep Learning 17: text classification with BERT using PyTorch
- Understanding Variational Graph Auto-Encoders
- Resources for BioNLP: datasets and tools
- Understanding Graph Convolutional Networks
- LectureBank: a dataset for NLP Education and Prerequisite Chain Learning
- ELMo in Practice
- Slideshare (4): A brief Introduction on Transfer Learning
- Transfer Learning Materials
- Slideshare (3): Unsupervised Transfer Learning Methods
- TensorFlow 08: save and restore a subset of variables
- To copy or not, that is the question: copying mechanism
- What matters: attention mechanism
- What’s next: seq2seq models
- Deep Learning 16: Understanding Capsule Nets
- Working with ROUGE 1.5.5 Evaluation Metric in Python
- Reinforcement Learning (1): Q-Learning basics
- Deep Learning 15: Unsupervised learning in DL? Try Autoencoder!
- Slideshare (2): Machine Learning Recap Slides sharing
- TensorFlow 07: Word Embeddings (2) – Loading Pre-trained Vectors
Other Activities
- Teaching Fellow for CPSC 477/577 Natural Language Processing at Yale (2020, Spring).
- NACLO 2020 Organizing Committee member
- Book Translation: AI Blueprints: How to build and deploy AI business projects (Origin) in progress, the Chinese version will be on sale very soon! (2019)
- Book Translation: Learning TensorFlow (O’Reilly Media) (Origin) Chinese version on sale now in jd.com (2018).
- Deep Learning Meetup @ Dublin, Ireland: organizer and committee member (2016-2017)
