Search Results for author:
Found 13 papers, 7 papers with code
Fact Checking Beyond Training Set
We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents.
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences
In our experiments, we observed that such a model is prone to mislabeling the source entities, which can often appear in the text, as the target entities.
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets
To overcome this challenge, we propose a hybrid metric to replace the plain confidence measurement.
Ericson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs.
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting
Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2. 5.
Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers
We present a novel multiple-source unsupervised model for text classification under domain shift.
Multi-View Active Learning for Short Text Classification in User-Generated Data
Our model has three novelties: 1) It is the first approach to employ multi-view active learning in this domain.
Semi-Supervised Text Classification via Self-Pretraining
This set is used to initialize the second classifier, to be further trained by the set of labeled documents.
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data
Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view.
Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons.
General Classification
Semi-Supervised Text Classification
+1
Mining Coronavirus (COVID-19) Posts in Social Media
World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020.
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.
