Related Experiment Video
Updated: Jun 11, 2025
06:37
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
2.6K
Research of multi-label text classification based on label attention and correlation networks.
Ling Yuan1, Xinyi Xu1, Ping Sun2
1School of Computing Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Plos One
|September 30, 2024
Summary
This study introduces Label Attention and Correlation Networks (LACN) to improve multi-label text classification. The model effectively handles complex label correlations and data imbalance, achieving strong performance on diverse datasets.
Area of Science:
- Natural Language Processing
- Machine Learning
- Artificial Intelligence
Background:
- Multi-Label Text Classification (MLTC) presents significant challenges.
- These include extracting local semantics, learning label correlations, and addressing data imbalance.
Purpose of the Study:
- To propose a novel model, Label Attention and Correlation Networks (LACN), for enhanced MLTC performance.
- To address the inherent complexities of MLTC, particularly label correlation and data imbalance.
Main Methods:
- Employs a label attention mechanism for discriminative text representation.
- Utilizes a correlation network based on label distribution to improve classification.
- Combines a weight factor and modulation function to mitigate label data imbalance.
Main Results:
- LACN demonstrates effectiveness on both conventional (AAPD, RCV1-v2) and extreme (EUR-LEX, AmazonCat-13K) datasets.
- Achieves optimal or suboptimal results compared to state-of-the-art methods.
- Outperforms the second-best method on the AAPD dataset in precision@k and NDCG@k.
Conclusions:
- The proposed LACN model is effective for extreme multi-label data.
- LACN shows competitiveness and superior outcomes in tackling MLTC tasks.
More Related Videos
Related Concept Videos
00:53Correlation and Regression
1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
01:30Classification of Signals
417
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
417
01:09Correlation
11.6K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.6K
01:29Aggregates Classification
305
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
305
02:20Correlations
32.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.7K
01:26Classification of Systems-I
176
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
176