122 citations · 218 across the 11 of their papers we have counts for
6 papers · 1 filter
A global analysis of metrics used for measuring performance in natural language processing
Kathrin Blagec, Georg Dorffner, Milad Moradi +2
Measuring the performance of natural language processing models is challenging. Traditionally used metrics, such as BLEU and ROUGE, originally devised for machine translation and s…
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Kathrin Blagec, Hong Xu, Asan Agibetov +1
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical…
Deep learning models are not robust against noise in clinical text
Milad Moradi, Kathrin Blagec, Matthias Samwald
Artificial Intelligence (AI) systems are attracting increasing interest in the medical domain due to their ability to learn complicated tasks that require human intelligence and ex…
Evaluating the Robustness of Neural Language Models to Input Perturbations
Milad Moradi, Matthias Samwald
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark dat…
Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts
Milad Moradi, Matthias Samwald
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical…
Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation
Jose Antonio Miñarro-Giménez, Oscar Marín-Alonso, Matthias Samwald
BACKGROUND: The amount of biomedical literature is rapidly growing and it is becoming increasingly difficult to keep manually curated knowledge bases and ontologies up-to-date. In…