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Found 26 papers, 4 papers with code
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic
Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity.
MNLP at MEDIQA 2021: Fine-Tuning PEGASUS for Consumer Health Question Summarization
This paper details a Consumer Health Question (CHQ) summarization model submitted to MEDIQA 2021 for shared task 1: Question Summarization.
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
In this work, we present a novel annotated corpus, the Pediatric Social History Annotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods with Large Language Models (LLMs).
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models
Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography.
MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection
This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection.
MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts
With this in mind, the eRisk 2023 Task 1 was designed to do exactly that: assess the relevance of different sentences to the symptoms of depression as outlined in the BDI questionnaire.
MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models
The Reddit Health Online Talk (RedHot) corpus contains posts from medical condition-related subreddits with annotations characterizing the patient experience and medical conditions.
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base.
Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task
The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes.
The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria
In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone.
Online User Profiling to Detect Social Bots on Twitter
The new proposed model for bot detection creates user profiles based on personal information such as age, personality, gender, education from users' online posts and introduces a machine learning model to detect social bots with high prediction accuracy based on personal information.
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model
The problem of online offensive language limits the health and security of online users.
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing
Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community.
Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model
Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images.
Transferability of Neural Network Clinical De-identification Systems
We measured: transferability from external sources; transferability across note types; the contribution of external source data when in-domain training data are available; and transferability across institutions.
Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context
We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in jointly learning Named Entity Recognition (NER) and Relation Extraction (RE).
Transfer Learning Approach for Arabic Offensive Language Detection System -- BERT-Based Model
In our study, we apply the principles of transfer learning cross multiple Arabic offensive language datasets to compare the effects on system performance.
Exploratory Arabic Offensive Language Dataset Analysis
This paper adding more insights towards resources and datasets used in Arabic offensive language research.
SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.
SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection
This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media.
Deep Learning for Identification of Adverse Effect Mentions In Twitter Data
Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019).
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error.
Feature-Augmented Neural Networks for Patient Note De-identification
In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system.
De-identification of Patient Notes with Recurrent Neural Networks
It yields an F1-score of 97. 85 on the i2b2 2014 dataset, with a recall 97. 38 and a precision of 97. 32, and an F1-score of 99. 23 on the MIMIC de-identification dataset, with a recall 99. 25 and a precision of 99. 06.
Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives
We analyze the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis.
