Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
复制标题
面向知识的卷积神经网络在自然语言文本因果关系提取中的应用
DOI：
10.1016/j.eswa.2018.08.009
复制
发表时间：
2019-01-01
影响因子：
8.5
通讯作者：
Mao, Kezhi
中科院分区：
文献类型：
--
作者：
Li, Pengfei;Mao, Kezhi
因果关系提取对于自然语言处理（NLP）来说是一项具有挑战性但非常重要的任务。有许多现有的方法来解决这个任务，无论是基于规则的（非统计）或基于机器学习的（统计）方法。对于基于规则的方法，需要大量的手工工作来构造手工模式，然而，由于自然语言中因果关系表达式的复杂性，精确度和召回率较低。对于基于机器学习的方法，目前的方法要么依赖于复杂的特征工程，这是容易出错的，或者依赖于大量的标记数据，这是不切实际的因果关系提取问题。为了解决上述问题，本文提出了一种面向知识的卷积神经网络（K-CNN）用于因果关系提取。K-CNN由一个面向知识的通道和一个面向数据的通道组成，前者结合了人类的先验知识来捕捉因果关系的语言线索，后者从数据中学习因果关系的其他重要特征。面向知识的通道中的卷积滤波器是从词汇知识库（如WordNet和FrameNet）自动生成的。我们提出了过滤器选择和聚类技术来降低维度并提高K-CNN的性能。此外，创建了用于识别因果关系的附加语义特征。三个数据集已被用于评估K-CNN有效地从文本中提取因果关系的能力，该模型优于当前最先进的关系提取模型。(C)2018爱思唯尔有限公司版权所有
Causal relation extraction is a challenging yet very important task for Natural Language Processing (NLP). There are many existing approaches developed to tackle this task, either rule-based (non-statistical) or machine-learning-based (statistical) method. For rule-based method, extensive manual work is required to construct handcrafted patterns, however, the precision and recall are low due to the complexity of causal relation expressions in natural language. For machine-learning-based method, current approaches either rely on sophisticated feature engineering which is error-prone, or rely on large amount of labeled data which is impractical for causal relation extraction problem. To address the above issues, we propose a Knowledge-oriented Convolutional Neural Network (K-CNN) for causal relation extraction in this paper. K-CNN consists of a knowledge-oriented channel that incorporates human prior knowledge to capture the linguistic clues of causal relationship, and a data-oriented channel that learns other important features of causal relation from the data. The convolutional filters in knowledge-oriented channel are automatically generated from lexical knowledge bases such as WordNet and FrameNet. We propose filter selection and clustering techniques to reduce dimensionality and improve the performance of K-CNN. Furthermore, additional semantic features that are useful for identifying causal relations are created. Three datasets have been used to evaluate the ability of K-CNN to effectively extract causal relation from texts, and the model outperforms current state-of-art models for relation extraction. (C) 2018 Elsevier Ltd. All rights reserved.