Deep learning in clinical natural language processing: a methodical review
Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
Text classification using embeddings: a survey
Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …
representing features, because it ignores word order and senses, which can vary with the …
Automated ICD-9 coding via a deep learning approach
ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to
describe a patient's diagnosis. Accurate automated ICD-9 coding is important because …
describe a patient's diagnosis. Accurate automated ICD-9 coding is important because …
Using the Tsetlin machine to learn human-interpretable rules for high-accuracy text categorization with medical applications
Medical applications challenge today's text categorization techniques by demanding both
high accuracy and ease-of-interpretation. Although deep learning has provided a leap …
high accuracy and ease-of-interpretation. Although deep learning has provided a leap …
History-based attention in Seq2Seq model for multi-label text classification
Y Xiao, Y Li, J Yuan, S Guo, Y Xiao, Z Li - Knowledge-Based Systems, 2021 - Elsevier
Multi-label text classification is an important yet challenging task in natural language
processing. It is more complex than single-label text classification in that the labels tend to …
processing. It is more complex than single-label text classification in that the labels tend to …
Towards a robust deep neural network in texts: A survey
Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …
image classification, speech recognition, and natural language processing (NLP)). However …
[HTML][HTML] A new text classification model based on contrastive word embedding for detecting cybersecurity intelligence in twitter
Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because
it allows security experts to respond cyber threats in advance. In this paper, we devise a new …
it allows security experts to respond cyber threats in advance. In this paper, we devise a new …
Multi-label classification of microblogging texts using convolution neural network
MA Parwez, M Abulaish - IEEE Access, 2019 - ieeexplore.ieee.org
Microblogging sites contain a huge amount of textual data and their classification is an
imperative task in many applications, such as information filtering, user profiling, topical …
imperative task in many applications, such as information filtering, user profiling, topical …
Automatic learning path creation using OER: a systematic literature mapping
A Siren, V Tzerpos - IEEE Transactions on Learning …, 2022 - ieeexplore.ieee.org
Learning paths are curated sequences of resources organized in a way that a learner has all
the prerequisite knowledge needed to achieve their learning goals. In this article, we …
the prerequisite knowledge needed to achieve their learning goals. In this article, we …
Detection of malicious javascript on an imbalanced dataset
NM Phung, M Mimura - Internet of Things, 2021 - Elsevier
In order to be able to detect new malicious JavaScript with low cost, methods with machine
learning techniques have been proposed and gave positive results. These methods focus …
learning techniques have been proposed and gave positive results. These methods focus …