情感分析中的跨域语义相似度度量与多源域自适应

Dipak Patel, Kiran R. Amin
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引用次数: 2

摘要

当各个领域缺乏标记数据时,领域适应变得至关重要。如果传统机器学习模型在一个域(称为源域或训练域)上进行训练,并对不同域(称为目标域或测试域,与源域不同)的数据进行分类,则其准确性会大大降低。机器需要在相应的域上进行训练以提高分类精度,但标记每个新域是一项复杂且耗时的任务。因此,需要领域自适应技术来解决数据标注问题。相似度度量对于从目标域中选择与源域匹配的重要枢轴特征起着至关重要的作用。本文引入了一种增强的交叉熵度量来匹配不同域的归一化频率分布,并找到了一个重要的域特定特征集。此外,提出了在多源域自适应模型中使用增强交叉熵测度的技术,对目标域数据进行有效分类。结果表明,使用我们的方法,效率提高了3.66%至9.09%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Cross-Domain Semantic Similarity Measure and Multi-Source Domain Adaptation in Sentiment Analysis
Domain adaptation becomes crucial when there is a lack of labelled data in various domains. The accuracy of traditional machine learning models degrades largely if they are trained on one domain (called the source or training domain) and classify the data of a different domain (called the target domain or test domain, which is different from the source domain). The machine needs to train on a corresponding domain to improve the classification accuracy, but labelling each new domain is a complex and time-consuming task. Hence, the domain adaptation technique is required to solve the issue of data labeling. The similarity measure plays a vital role in selecting important pivot features from the target domain that match source domains. This research article has introduced an enhanced cross-entropy measure for matching the normalized frequency distribution of different domains and found an important domain-specific feature set. In addition, the technique of using enhanced cross entropy measures is proposed in the multi-source domain adaptation model to effectively classify the target domain data. The result shows that there is an improvement of 3.66% to 9.09% using our approach.
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