In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.
Coreference resolution over semantic graphs like AMRs aims to group the graph nodes that represent the same entity.
1 code implementation • 28 Nov 2023 • Rui Yang, Qingcheng Zeng, Keen You, Yujie Qiao, Lucas Huang, Chia-Chun Hsieh, Benjamin Rosand, Jeremy Goldwasser, Amisha D Dave, Tiarnan D. L. Keenan, Emily Y Chew, Dragomir Radev, Zhiyong Lu, Hua Xu, Qingyu Chen, Irene Li
This study introduces MedGen, a comprehensive natural language processing (NLP) toolkit designed for medical text processing.
Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP).
Large Language Models (LLMs) have achieved significant success across various natural language processing (NLP) tasks, encompassing question-answering, summarization, and machine translation, among others.
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests.
Current text-based models utilize the entity name and description to infer the tail entity given the head entity and a certain relation.
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity.
The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research.
In this paper, we propose the educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains.
1 code implementation • 16 Dec 2021 • Swapnil Hingmire, Irene Li, Rena Kawamura, Benjamin Chen, Alexander Fabbri, Xiangru Tang, Yixin Liu, Thomas George, Tammy Liao, Wai Pan Wong, Vanessa Yan, Richard Zhou, Girish K. Palshikar, Dragomir Radev
We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject.
Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely.
no code implementations • 7 Jul 2021 • Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlali, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz, Dragomir Radev
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV).
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
no code implementations • 10 Jun 2020 • Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao, Irene Li, Hiroshi Maruyama, Hiroki Kanezashi, Raquel P'erez-Arnal, Kunihiko Miyoshi, Euma Ishii, Keita Suzuki, Sayaka Shiba, Mariko Kurokawa, Yuta Kanzawa, Naomi Nakagawa, Masatoshi Hanai, Yixin Li, Tianxiao Li
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent.
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.
Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation.
Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community.
Ranked #1 on Scientific Document Summarization on CL-SciSumm
4 code implementations • • 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, Dragomir Radev
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.
Ranked #5 on Multi-Document Summarization on Multi-News
The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation.
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #10 on Semantic Parsing on spider
The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources.
We present an approach to automatically classify clinical text at a sentence level.