首页> 外文OA文献 >Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification
【2h】

Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification

机译:跨语言和跨领域情感分类的分布对应索引

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "targetu27u27 domain when the only available training data belongs to a different "sourceu27u27 domain. In this paper we present the Distributional Correspondence Indexing (DCI) method for domain adaptation in sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. Term correspondence is quantified by means of a distributional correspondence function (DCF). We propose a number of efficient DCFs that are motivated by the distributional hypothesis, i.e., the hypothesis according to which terms with similar meaning tend to have similar distributions in text. Experiments show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification. DCI also brings about a significantly reduced computational cost, and requires a smaller amount of human intervention. As a final contribution, we discuss a more challenging formulation of the domain adaptation problem, in which both the cross-domain and cross-lingual dimensions are tackled simultaneously.
机译:域适应(DA)技术旨在使机器学习方法能够在唯一可用的训练数据属于不同的“源 u27 u27”域时为“目标 u27 u27”域学习有效的分类器。在本文中,我们提出了用于情感分类领域适应的分布对应索引(DCI)方法。 DCI在两个域共有的向量空间中得出术语表示,其中每个维度都反映了其与支点的分布对应关系,即与在整个域中表现相似的高度预测性术语相关。术语对应关系通过分布对应函数(DCF)进行量化。我们提出了许多有效的DCF,这些DCF受到分布假设(即根据这种假设,具有相似含义的术语趋向于在文本中具有相似分布)的启发。实验表明,对于跨语言和跨域情感分类,DCI的性能要优于当前的最新技术。 DCI还带来了显着降低的计算成本,并且需要较少的人工干预。作为最后的贡献,我们讨论了领域适应性问题的更具挑战性的表述,其中跨域和跨语言的维度同时得到解决。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号