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Found 16 papers, 9 papers with code
A Method to Generate a Machine-Labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains
To generate a large amount of machine-labeled data, firstly we generate an initial machine-labeled data by using a chunker and MetaMap.
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph
Word sense disambiguation (WSD) is the task to determine the word sense according to its context.
Why Do Masked Neural Language Models Still Need Common Sense Knowledge?
From the test, we observed that MNLMs partially understand various types of common sense knowledge but do not accurately understand the semantic meaning of relations.
Word Sense Disambiguation using Knowledge-based Word Similarity
Furthermore, our system outperformed the existing knowledge-based WSD systems and showed a performance comparable to that of the state-of-the-art supervised WSD systems.
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?
We exemplify the possibility to overcome the limitations of the MNLM-based RC models by enriching text with the required knowledge from an external commonsense knowledge repository in controlled experiments.
MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score
We first present a novel and publicly available dataset with expert-annotated medical jargon terms from 18K+ EHR note sentences ($MedJ$).
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
This task is challenging due to the high-dimensional space of multi-label assignment (155, 000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically.
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning
The transfer learning framework helps SOAP classification model's inter-hospital migration with a minimal size of the manually annotated dataset.
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided.
ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detection
Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change).
CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports
In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports.
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR).
Is it safe to cross? Interpretable Risk Assessment with GPT-4V for Safety-Aware Street Crossing
Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues.
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
The advancement of natural language processing (NLP) systems in healthcare hinges on language model ability to interpret the intricate information contained within clinical notes.
The Global Banking Standards QA Dataset (GBS-QA)
A domain specific question answering (QA) dataset dramatically improves the machine comprehension performance.
