Deep learning provides a new modeling method for natural language processing. In recent years, it has been applied in language model, text classification, machine translation, sentiment analysis, question and answer system, word distributed representation, etc., and a series of theoretical research results have been obtained. For the text representation task, this paper studies the strategy of fusing global and local context information, and proposes a word representation model called Topic-based CBOW that integrates deep neural network, topic information and word order information. Then, based on the word distributed representation obtained by Topic-based CBOW, a short text representation method with TF–IWF-weighted pooling is proposed. Finally, the performance of the Topic-based CBOW model and the short text representation are compared with the baseline models, and it is found that the proposed method improves the quality of the word distributed representation to some extent by introducing the topic vector and retaining word order information, and text representation also performs well in text classification tasks.

中文翻译：

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基于深度学习和主题信息的文本表示方法研究

深度学习为自然语言处理提供了一种新的建模方法。近年来，它在语言模型、文本分类、机器翻译、情感分析、问答系统、词分布式表示等方面得到了应用，并取得了一系列理论研究成果。针对文本表示任务，本文研究了融合全局和局部上下文信息的策略，提出了一种融合深度神经网络、主题信息和词序信息的词表示模型，称为Topic-based CBOW。然后，基于Topic-based CBOW得到的词分布式表示，提出了一种TF-IWF-weighted pooling的短文本表示方法。最后，