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Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification
Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …
natural language processing. Previous studies mainly focus on learning text representation …
Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021
The present study analyzed over 26,000 research articles published between 1991 and
2021 in twenty-one major LIS (Library and Information Science) journals, using the machine …
2021 in twenty-one major LIS (Library and Information Science) journals, using the machine …
CNN-BiLSTM-Attention: A multi-label neural classifier for short texts with a small set of labels
We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese
posted by users on government web portals, so that a message can be directed to one or …
posted by users on government web portals, so that a message can be directed to one or …
[HTML][HTML] A new hybrid based on long short-term memory network with spotted hyena optimization algorithm for multi-label text classification
An essential work in natural language processing is the Multi-Label Text Classification
(MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional …
(MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional …
Identifying security and privacy violation rules in trigger-action IoT platforms with NLP models
Trigger-action platforms are systems that enable users to easily define, in terms of
conditional rules, custom behaviors concerning Internet of Things (IoT) devices and Web …
conditional rules, custom behaviors concerning Internet of Things (IoT) devices and Web …
[HTML][HTML] Optimal performance of Binary Relevance CNN in targeted multi-label text classification
In the context of multi-label text classification (MLTC), Binary Relevance (BR) stands out as
one of the most intuitive and frequently employed methodologies. It tackles the MLTC task by …
one of the most intuitive and frequently employed methodologies. It tackles the MLTC task by …
Enhancing label correlation feedback in multi-label text classification via multi-task learning
In multi-label text classification (MLTC), each given document is associated with a set of
correlated labels. To capture label correlations, previous classifier-chain and sequence-to …
correlated labels. To capture label correlations, previous classifier-chain and sequence-to …
Standard ner tagging scheme for big data healthcare analytics built on unified medical corpora
The motivation for this research comes from the gap found in discovering the common
ground for medical context learning through analytics for different purposes of diagnosing …
ground for medical context learning through analytics for different purposes of diagnosing …
SHO-CNN: A metaheuristic optimization of a convolutional neural network for multi-label news classification
News media always pursue informing the public at large. It is impossible to overestimate the
significance of understanding the semantics of news coverage. Traditionally, a news text is …
significance of understanding the semantics of news coverage. Traditionally, a news text is …
Multi-label emotion classification based on adversarial multi-task learning
N Lin, S Fu, X Lin, L Wang - Information Processing & Management, 2022 - Elsevier
In this paper, we focus on the task of multi-label emotion classification and aim to tackle two
problems of this task. First, few studies try to exploit the correlation among different emotions …
problems of this task. First, few studies try to exploit the correlation among different emotions …