论文标题
使用卷积神经网络的核心分辨率系统使用提到配对方法和Singleton排除
Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network
论文作者
论文摘要
神经网络在使用提及的方法的核心分辨率系统上显示出令人鼓舞的性能。有了深层的神经网络，它可以学习两个提及之间的隐藏和深厚的关系。但是，对于使用这种学习技术的印尼文本的核心分辨率没有工作。印尼文本的最新系统仅指出使用词汇和句法功能可以改善现有的核心分辨率系统。在本文中，我们提出了一个新的核心分辨率系统，该系统针对印尼文本提到了一对方法，该方法使用深度神经网络来了解这两个提及的关系。除了词汇和句法特征外，为了了解提到单词和上下文的表示，我们使用单词嵌入并将其馈送到卷积神经网络（CNN）。此外，我们确实使用Singleton分类器组件进行Singleton排除，以防止Singleton提到最后进入任何实体群集。我们提出的系统以平均F1得分获得了67.37％的无辛格尔顿排除，拥有训练有素的单顿分类器的63.27％，而Gold Singleton分类器的75.95％优于最先进的系统。
Neural network has shown promising performance on coreference resolution systems that uses mention pair method. With deep neural network, it can learn hidden and deep relations between two mentions. However, there is no work on coreference resolution for Indonesian text that uses this learning technique. The state-of-the-art system for Indonesian text only states the use of lexical and syntactic features can improve the existing coreference resolution system. In this paper, we propose a new coreference resolution system for Indonesian text with mention pair method that uses deep neural network to learn the relations of the two mentions. In addition to lexical and syntactic features, in order to learn the representation of the mentions words and context, we use word embeddings and feed them to Convolutional Neural Network (CNN). Furthermore, we do singleton exclusion using singleton classifier component to prevent singleton mentions entering any entity clusters at the end. Achieving 67.37% without singleton exclusion, 63.27% with trained singleton classifier, and 75.95% with gold singleton classifier on CoNLL average F1 score, our proposed system outperforms the state-of-the-art system.
