Explaining Contextualized Word Embeddings in Biomedical Research - A Qualitative Investigation
Contextualized word embeddings proved to be highly successful quantitative representations of words that allow to efficiently solve various tasks such as clinical entity normalization in unstructured texts. In this paper, we investigate how the Saussurean sign theory can be used as a qualitative exp...
Main Authors: | Miletic, M. (Author), Sariyar, M. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
NLM (Medline)
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
French AXA Insurance Word Embeddings : Effects of Fine-tuning BERT and Camembert on AXA France’s data
by: Zouari, Hend
Published: (2020) -
Detecting White Supremacist Hate Speech Using Domain Specific Word Embedding With Deep Learning and BERT
by: Hind S. Alatawi, et al.
Published: (2021-01-01) -
Text Mining of Stocktwits Data for Predicting Stock Prices
by: Mukul Jaggi, et al.
Published: (2021-02-01) -
Understandable and trustworthy explainable robots: A sensemaking perspective
by: Papagni Guglielmo, et al.
Published: (2020-10-01) -
Banner: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition
by: Imranul Ashrafi, et al.
Published: (2020-01-01)