CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Thesis Advisor宗成庆
Degree Grantor中国科学院大学
Place of Conferral自动化研究所
Degree Name工学博士
Degree Discipline模式识别与智能系统
Keyword自然语言理解 篇章关系识别 接地语言学习 基于经验的篇章因果关系识别 基于期望的篇章转折关系识别





(2)针对目前篇章关系识别模型的局限性,提出了一种基于经验的篇章因果关系识别模型。它受人类篇章理解过程的启发,通过积累不同场景中的经验信息以识别篇章因果关系。在实现中,所提模型采用了接地语言学习(grounded language learning)技术框架,可以通过环境直接为模型提供经验。



Other Abstract

People often refer to a series of semantically related sentences as discourse. In daily life, people are used to using discourse to express their intentions and ideas. Among them, the discourse relation (structure) plays a very important role, which links the different parts of contexts of the discourse, so that these isolated segments have integral and coherent semantics. The discourse relations can also directly influence the meaning of the discourse. Thus, in order to comprehend the discourse accurately, one must first understand the discourse relation.
In addition, previous studies have pointed out that discourse relation information is also very beneficial to downstream natural language processing tasks. Therefore, the issue of discourse relation recognition has important research value both in theory and in practice.

At present, the mainstream research on discourse relation recognition adopts the methodology based on text clues. The core idea of this methodology is to investigate and analyze the statistical patterns between textual clues and discourse relations, and to construct the statistical machine learning model to identify the discourse relation. This approach has made some progress, but it has also encountered a series of problems. Some studies have pointed out that such an approach neither reflect the inner semantics of the discourse context nor conform to the facts and characteristics of human discourse relation understanding.

According to the above problems, this paper focuses on how to construct a reasonable and effective text relationship recognition model, and designs and constructs a discourse relation recognition model by imitating the process of human. The contributions of this paper are summarized as follows:

(1) For Chinese implicit discourse relation recognition problem, we propose a memory augmented attention-based neural network model. The model introduces the attention mechanism to optimize the  representation of the discourse context, and uses the memory network to cache the context pattern of the discourse relation, thereby improving the efficiency of the discourse relation classification. Experiments have shown that the proposed model has achieved comparable performance to the best models in the public data set. 

(2) Current mainstream models and methods use context information for discourse relation recognition only, then we propose an experience-based discourse causality recognition model which is inspired by the human discourse understanding processing. It first accumulates experience information in different scenes and uses experience information to identify causality in discourse context. In the implementation, we adopt the grounded language learning technology framework which can build experience (of environments) for the model. Therefore, when the model processes the text, the experience information stored in the memory can provide the rich semantic information of the discourse. Experiments show that the proposed model is significantly superior to the traditional text-based recognition model, and the proposed model has better interpretability.

(3) For concession recognition problem, we propose a concession relation recognition model based on expectation comparison. This model intimates the process of human concession understanding in which people usually generate expectations based on the preceding context and experience first, then compare the expectations with the following context to identify the concession relation. Experiments show that the proposed model has obvious advantages over existing text-based models. Further analysis indicates that the proposed model has good interpretability.

In summary, this paper is committed to constructing a more effective discourse relation recognition model. In particular, we introduce a grounded language learning technology framework, and construct corresponding grounded discourse relation data sets and experience-based discourse relation recognition models under the guidance of cognitive linguistics. We demonstrate the potential and superiority of the new approach have provided new ideas for the field and have strongly promoted research in this area.

Document Type学位论文
Recommended Citation
GB/T 7714
刘洋. 篇章关系识别方法研究与应用[D]. 自动化研究所. 中国科学院大学,2019.
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