MuTCELM:一种基于CNN的最佳多文本集体学习,用于文本分类
Victor Kwaku Agbesi1, Wenyu Chen1, Sophyani Banaamwini Yussif1
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China.
Heliyon
|October 18, 2024
概括
一种新的特征提取方法,多文本CNN,通过捕获多层次信息来增强文本分类. 集成该方法的MuTCELM模型在准确性和其他指标方面显示出显著的性能增长.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 有效的特征提取对于文本分类至关重要,将文本转换为机器学习的数字数据.
- 现有的方法难以捕获多样化的语义和上下文信息,导致性能不佳和过度匹配.
- 以前的特征提取技术的局限性阻碍了文本分类准确性的进步.
研究的目的:
- 介绍Multi-TextCNN,这是一个高级的功能提取器,用于捕获多层次的文本信息.
- 提出MuTCELM模型,整合多文本CNN以提高文本分类性能.
- 评估MuTCELM与各种数据集和语言的基线模型的有效性.
主要方法:
- 开发了多文本CNN以提取多个细分级别的特征,解决语义和上下文捕获.
- 将多文本CNN集成到MuTCELM分类模型中,使用五个不同的子分类器.
- 采用整体框架,将子分类器的优势结合起来,以提高整体性能.
主要成果:
- MuTCELM在准确度,精度,回忆和F1宏分数中平均有0.2584,0.2546,0.2668和0.2612的改善.
- 经验结果显示,在所有测试数据集中,与基线模型相比,性能显著提高.
- 统计分析证实了不重叠的置信区间,表明了有意义和强大的业绩改善.
结论:
- 多文本CNN功能提取器有效地捕获了跨多个细分级别的基本文本信息.
- MuTCELM模型,利用多文本CNN和整体方法,显著提高了文本分类性能.
- MuTCELM在各种语言和文本分类任务中表现出强度和优越性,优于现有的合奏方法.
相关概念视频
01:22Multi-input and Multi-variable systems
100
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
100
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
01:31Classification of Systems-II
136
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
136
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
01:30Classification of Signals
412
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...
412
02:07Improving Translational Accuracy
9.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.3K