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	1. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization
	Li Yufeng,Xu Weiran
	Computer Science and Technology 24 December 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (433K B)

	Abstract:Automatic text summarization is an important field for NLP, which includes the extractive and the abstractive method. Among many languages, Chinese has many special properties, such as rich character semantic expressions, flexible abbreviation. Moreover, insufficient training samples are also a problem. In this paper, we propose a dual-attentive and word-character Chinese text summarization model. The hybrid word-character approach (HWC) will preserve the advantages of both word based and character-based representations, which are very suitable for Chinese language. And the extractive and abstractive methods are combined to accurately capture the key information and gain the essence of articles with less supervised samples. We evaluate our model using the ROUGE evaluation on a widely used Chinese Dataset LCSTS2.0. The experimental results show that the model is very effective.
	TO cite this article:Li Yufeng,Xu Weiran. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization[OL].[24 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753279


	2. Relation Extraction with Domain Adversarial Neural Network and Graphical Model
	MA Kuo,ZHANG Xi
	Computer Science and Technology 23 July 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (753K B)

	Abstract:People read a comment on the web to learn about a product or some news, and the adjectives and nouns in these comments express important information. When we extract the adjective and nouns in the comments, If we can determine that there is indeed a relationship between the adjective and the noun, it will be very helpful for us to understand the comment. This thesis is all about extracting these word pairs and using transfer learning to extract them more quickly and accurately. This adjective and noun pair may undergo some changes in their relationship in different domains. This thesis considers the different domains to identify whether they are related or not. In this paper we propose an adversarial neural network approach with the help of a graphical model, DANN-G. This method considers the relationship between the bags well, the relationship within the bags, and thus reduces the noise caused by remote supervision in the common methods of relationship extraction. Our model has improved in the five major data sets of Amazon.
	TO cite this article:MA Kuo,ZHANG Xi. Relation Extraction with Domain Adversarial Neural Network and Graphical Model[OL].[23 July 2020] http://en.paper.edu.cn/en_releasepaper/content/4752560


	3. A Keyword extraction method based on Neural Networks with Joint Training
	You Huanying,She Chundong,Liu Shaohua
	Computer Science and Technology 07 March 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (438K B)

	Abstract:Keyword extraction technology has gradually become a hot research problem in Natural Language Processing (NLP) and Information Retrieval. Many language tasks are inseparable from keyword extraction technology, such as long text classification, automatic summary, machine translation, dialogue system, etc. In this paper, we design a keyword extraction algorithm that can combine the benefits of both memorization and generalization. Our model contains a linear model and a deep neural networks. The linear model learns the relationship between statistic features and keywords, which can make full use of the memory capabilities of the shallow model. In the deep component, we feed the projection vector of words on the text to deep neural networks, which can enhance the generalization ability of the model. With the joint training of the linear model and the deep neural networks, our model achieves higher accuracy and scalability. Our method is compared with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF) and TextRank. On the same batch of test dataset, our model is superior to the baseline model in Precision, Recall, and F-score, respectively.
	TO cite this article:You Huanying,She Chundong,Liu Shaohua. A Keyword extraction method based on Neural Networks with Joint Training[OL].[ 7 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751038


	4. Conversational Recommendation System based on Sentiment Analysis
	LI Xinsheng,LI Jian
	Computer Science and Technology 25 February 2020
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (772K B)

	Abstract:The combination of the recommender system and dialogue system which called the conversational recommendation system is a growing interest. Tosolve the problem that it is difficult to obtain users' tastes in conversational recommendation systems. A sentiment analysis method is proposed in our conversational recommendation model to get user preferences. A sentiment analysis dataset is created and the model uses a sentiment analysis approach to obtain a movie seeker\'s preferences and make a recommendation. Experimentresults show that our sentiment analysis model yields a better performance of 0.8362(F1 score) than the baseline(0.7802) and other models. Thus, the movie recommended by our system can meet the needs of users better.
	TO cite this article:LI Xinsheng,LI Jian. Conversational Recommendation System based on Sentiment Analysis[OL].[25 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750892

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