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Found 10 papers, 7 papers with code
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension
Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.
An empirical study of next-basket recommendations
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
Medical Question Summarization with Entity-driven Contrastive Learning
Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance.
A Systematical Evaluation for Next-Basket Recommendation Algorithms
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
Aspect-driven User Preference and News Representation Learning for News Recommendation
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain.
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning.
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.
Self-Attention Enhanced Patient Journey Understanding in Healthcare System
The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes.
Temporal Self-Attention Network for Medical Concept Embedding
In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.
