在情感倾向性分析中,经常会发生由于领域知识的变化引起的分类精度下降的问题。为解决此类问题,该文提出了一种基于实例和特征相融合的知识迁移方法,首先通过三部图构建了源领域和目标领域的领域依赖特征词之间的关联,并得到一个公共的语义空间来对原有的向量空间模型进行重建,然后再通过带偏置的马尔科夫模型,建立源领域和目标领域实例之间的关联,从而有效的将源领域学习到的情感倾向性知识迁移到目标领域中,高于其它方法的实验结果验证了算法的有效性。
Abstract
The accuracy decrease across different domains is commor in current sentiment analysis. To solve the problem, this paper presents a knowledge transferring approach based on the combination of the features and the instancetransfer. Firstly, the proposed approach builds the relevance of the domain dependent features between the source domain and the target domain via a tripartite graph so that a common semantic space is projected to rebuild the original vector space model. Then the proposed approach builds the relevance of the instances between the source domain and the target domain via a biased Markov model. This approach transfers sentiment analysis knowledge from the source domain to the target domain. The enhanced experimental performance confirms the effectiveness of the approach.
关键词
跨领域倾向性分析 /
迁移学习 /
偏置的马尔科夫模型
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Key words
cross-domain sentiment analysis /
transfer learning /
biased Markov model
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参考文献
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脚注
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基金
国家自然科学基金(61202254); 高校自主科研基金(DC201502030202, DC201502030405)
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