基于标签注意力和相关性网络的多标签文本分类研究
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
概括
本研究介绍了标签注意力和相关性网络 (LACN),以改进多标签文本分类. 该模型有效地处理复杂的标签相关性和数据不平衡,在各种数据集上实现强的性能.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 多标签文本分类 (MLTC) 提出了重大挑战.
- 这些包括提取本地语义,学习标签相关性和解决数据不平衡.
研究的目的:
- 提出一个新的模型,标签注意力和相关性网络 (LACN),以提高MLTC的性能.
- 解决MLTC固有的复杂性,特别是标签相关性和数据不平衡.
主要方法:
- 采用标签注意力机制,用于歧视性文本表示.
- 使用基于标签分布的相关性网络来改善分类.
- 结合重量因子和调制功能,以减轻标签数据不平衡.
主要成果:
- 在传统 (AAPD,RCV1-v2) 和极端 (EUR-LEX,AmazonCat-13K) 数据集上,LACN表现出有效性.
- 与最先进的方法相比,实现最佳或次优的结果.
- 在precision@k和NDCG@k.中优于AAPD数据集中的第二最佳方法.
结论:
- 拟议的LACN模型对于极端的多标签数据是有效的.
- 在解决MLTC任务方面,LACN表现出竞争力和优异的结果.
相关概念视频
Correlation 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
Classification 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
Correlation
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
Aggregates 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
Correlations
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
Classification 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


