Building Context-aware Clause Representations for Situation Entity Type Classification-猫眼课题宝

DOI：

10.18653/v1/d18-1368

发表时间：

2018-09

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Zeyu Dai;Ruihong Huang

根据小句引入语篇的情景实体(例如，事件、状态和一般陈述)的类型来对小句进行分类的能力可以使许多NLP应用受益。注意到小句的情景实体类型取决于小句在段落中发挥的语篇功能，而语篇功能的解释在很大程度上依赖于整个段落的语境，我们建议建立上下文感知的小句表征来预测小句的情景实体类型。具体地说，我们提出了一种分层递归神经网络模型，通过对子句的上下文影响和相互依赖进行广泛的建模，一次阅读整个段落，并联合学习该段中所有子句的表示。实验结果表明，我们的模型在语类丰富的MASC+Wiki语料库上达到了最先进的小句级情景实体分类性能，接近人类水平。

Capabilities to categorize a clause based on the type of situation entity (e.g., events, states and generic statements) the clause introduces to the discourse can benefit many NLP applications. Observing that the situation entity type of a clause depends on discourse functions the clause plays in a paragraph and the interpretation of discourse functions depends heavily on paragraph-wide contexts, we propose to build context-aware clause representations for predicting situation entity types of clauses. Specifically, we propose a hierarchical recurrent neural network model to read a whole paragraph at a time and jointly learn representations for all the clauses in the paragraph by extensively modeling context influences and inter-dependencies of clauses. Experimental results show that our model achieves the state-of-the-art performance for clause-level situation entity classification on the genre-rich MASC+Wiki corpus, which approaches human-level performance.

Building Context-aware Clause Representations for Situation Entity Type Classification

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