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首页> 外文会议>International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management >Cross-domain In-domain Sentiment Analysis with Memory-based Deep Neural Networks
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Cross-domain In-domain Sentiment Analysis with Memory-based Deep Neural Networks

机译：基于内存的深神经网络跨域和域域情绪分析

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Cross-domain sentiment classifiers aim to predict the polarity, namely the sentiment orientation of target text documents, by reusing a knowledge model learned from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Distributed word representations are able to capture hidden word relationships without supervision, even across domains. Deep neural networks with memory (MemDNN) have recently achieved the state-of-the-art performance in several NLP tasks, including cross-domain sentiment classification of large-scale data. The contribution of this work is the massive experimentations of novel outstanding MemDNN architectures, such as Gated Recurrent Unit (GRU) and Differentiable Neural Computer (DNC) both in cross-domain and in-domain sentiment classification by using the GloVe word embeddings. As far as we know, only GRU neural networks have been applied in cross-domain sentiment classification. Sentiment classifiers based on these deep learning architectures are also assessed from the viewpoint of scalability and accuracy by gradually increasing the training set size, and showing also the effect of fine-tuning, an explicit transfer learning mechanism, on cross-domain tasks. This work shows that MemDNN based classifiers improve the state-of-the-art on Amazon Reviews corpus with reference to document-level cross-domain sentiment classification. On the same corpus, DNC outperforms previous approaches in the analysis of a very large in-domain configuration in both binary and fine-grained document sentiment classification. Finally, DNC achieves accuracy comparable with the state-of-the-art approaches on the Stanford Sentiment Treebank dataset in both binary and fine-grained single-sentence sentiment classification.
机译：跨域情绪分类器旨在通过重用从不同来源域中学习的知识模型来预测极性，即目标文献文档的情绪取向。不同的域通常是异构的语言，因此可以建议转移学习技术支持从源到目标的知识转移。即使跨域，分布式字表示能够在没有监督的情况下捕获隐藏的词关系。具有内存（MEMDNN）的深度神经网络最近在几个NLP任务中实现了最先进的性能，包括大规模数据的跨域情绪分类。这项工作的贡献是通过使用手套单词嵌入的跨域和域名情绪分类，如新颖的优秀MEMDNN架构的巨大实验，例如跨域和域情绪分类的门控经常性单元（GRU）和可分辨率的神经计算机（DNC）。据我们所知，只有GRU神经网络已经应用于跨域情绪分类。根据可扩展性和准确性的观点来评估基于这些深度学习架构的情感分类器通过逐步增加训练集规模，并且还显示微调，显式传输学习机制，跨域任务的效果。这项工作表明，基于Memdnn的分类器在亚马逊的亚马逊审查语料库上提高了文档级跨域情绪分类。在相同的语料库上，DNC在二进制和细粒度文献情绪分类中分析了对非常大的域配置中的先前方法。最后，DNC在二进制和细粒度单句话情绪分类中实现了与斯坦福情绪树数据集上的最先进方法相当的准确性。

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