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Found 15 papers, 6 papers with code
Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging
We conducted a comprehensive empirical study to demonstrate the superiority of DREAM, including sleep stage prediction experiments, a case study, the usage of unlabeled data, and uncertainty.
Fairness and Accuracy under Domain Generalization
Although many approaches have been proposed to make ML models fair, they typically rely on the assumption that data distributions in training and deployment are identical.
Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning
In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features.
TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding
International Classification of Disease (ICD) coding procedure which refers to tagging medical notes with diagnosis codes has been shown to be effective and crucial to the billing system in medical sector.
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
The current state-of-the-art (SOTA) model for FG-NER relies heavily on manual efforts for building a dictionary and designing hand-crafted features.
An Empirical Study on Fine-Grained Named Entity Recognition
However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages.
On the Use of Machine Translation-Based Approaches for Vietnamese Diacritic Restoration
This paper presents an empirical study of two machine translation-based approaches for Vietnamese diacritic restoration problem, including phrase-based and neural-based machine translation models.
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing
This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition.
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit
This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, named entity recognition (NER).
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition
This paper presents a state-of-the-art system for Vietnamese Named Entity Recognition (NER).
Content-based Approach for Vietnamese Spam SMS Filtering
In this work, we propose the first system for filtering Vietnamese spam SMS.
Building a Semantic Role Labelling System for Vietnamese
This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level
This paper demonstrates end-to-end neural network architectures for Vietnamese named entity recognition.
