Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks.
Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is of critical importance.
In several domains, data objects can be decomposed into sets of simpler objects.
Ranked #1 on Document Classification on Twitter
Word embeddings are undoubtedly very useful components in many NLP tasks.
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential.
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words(GoW) model in which each document is represented by a graph that encodes relationships between the different terms.
Graph kernels have been successfully applied to many graph classification problems.