Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts

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 wor...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Pengfei, Mao, Kezhi
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/136855
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-136855
record_format dspace
spelling sg-ntu-dr.10356-1368552021-01-28T08:39:53Z Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts Li, Pengfei Mao, Kezhi School of Electrical and Electronic Engineering Engineering::Computer science and engineering Natural Language Processing Convolutional Neural Network 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. Accepted version 2020-01-31T05:13:50Z 2020-01-31T05:13:50Z 2019 Journal Article Li, P., & Mao, K. (2019). Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts. Expert Systems with Applications, 115512-523. doi:10.1016/j.eswa.2018.08.009 0957-4174 https://hdl.handle.net/10356/136855 10.1016/j.eswa.2018.08.009 2-s2.0-85052130680 115 512 523 en Expert Systems with Applications © 2019 Elsevier. All rights reserved. This paper was published in Expert Systems with Applications and is made available with permission of Elsevier. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Natural Language Processing
Convolutional Neural Network
spellingShingle Engineering::Computer science and engineering
Natural Language Processing
Convolutional Neural Network
Li, Pengfei
Mao, Kezhi
Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Pengfei
Mao, Kezhi
format Article
author Li, Pengfei
Mao, Kezhi
author_sort Li, Pengfei
title Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
title_short Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
title_full Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
title_fullStr Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
title_full_unstemmed Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
title_sort knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
publishDate 2020
url https://hdl.handle.net/10356/136855
_version_ 1690658439936606208