Search Results for author:
Found 36 papers, 19 papers with code
Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting
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.
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution
Coreference resolution over semantic graphs like AMRs aims to group the graph nodes that represent the same entity.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities.
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
We assess LLMs' zero-shot performance in creating domain-specific concept graphs and introduce TutorQA, a new expert-verified NLP-focused benchmark for scientific graph reasoning and QA.
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings.
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models
In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP).
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update.
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests.
HiPool: Modeling Long Documents Using Graph Neural Networks
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem.
NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods
Current text-based models utilize the entity name and description to infer the tail entity given the head entity and a certain relation.
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity.
EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts
The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research.
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation
In this paper, we propose the educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains.
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject.
Surfer100: Generating Surveys From Web Resources, Wikipedia-style
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.
Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains
Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator.
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders
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).
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
Detecting Bias in Transfer Learning Approaches for Text Classification
In a supervised learning setting, labels are always needed for the classification task.
Global Data Science Project for COVID-19
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning
The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent.
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation.
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
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
SParC: Cross-Domain Semantic Parsing in Context
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.
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
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
What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
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
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
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.
Medical Text Classification using Convolutional Neural Networks
We present an approach to automatically classify clinical text at a sentence level.
