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Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, Abir Naskar
2018 Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue  
In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text.  ...  The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes.  ...  Conclusion In this paper, we present a linguistically informed deep neural network architecture for the automatic extraction of cause-effect relations from text documents.  ... 
doi:10.18653/v1/w18-5035 dblp:conf/sigdial/DasguptaSDN18 fatcat:zxsscfmmh5hrnpyptwgzfx6drq

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

Wajid Ali, Wanli Zuo, Rahman Ali, Xianglin Zuo, Gohar Rahman
2021 Applied Sciences  
Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers.  ...  Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making  ...  For event context word extension, they used BK, extracted from news articles in the form of a causal network to identify event causality.  ... 
doi:10.3390/app112110064 fatcat:btv66da5x5a73auogv5d3lp2bi

A Survey on Extraction of Causal Relations from Natural Language Text [article]

Jie Yang, Soyeon Caren Han, Josiah Poon
2021 arXiv   pre-print
As an essential component of human cognition, cause-effect relations appear frequently in text, and curating cause-effect relations from text helps in building causal networks for predictive tasks.  ...  Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems.  ...  From their point of view, instead of using manually linguistic clues and domain knowledge, semi-automatic learning acquires lexico-syntactic patterns from a larger corpus automatically.  ... 
arXiv:2101.06426v2 fatcat:hd3ikb7mejcndlq6wsgojv4uoa

Causal Relation Identification Using Convolutional Neural Networks And Knowledge Based Features

Tharini N. De Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi
2017 Zenodo  
The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations.  ...  Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification.  ...  Khoo et al. use predefined linguistic patterns to extract causal relations from medical newspaper text, managing to achieve a precision of approximately 68%.  ... 
doi:10.5281/zenodo.1130678 fatcat:alglobtjb5ax7hrnkmhzk7ljtm

Inter-sentence and Implicit Causality Extraction from Chinese Corpus [chapter]

Xianxian Jin, Xinzhi Wang, Xiangfeng Luo, Subin Huang, Shengwei Gu
2020 Lecture Notes in Computer Science  
Automatically extracting causal relations from texts is a challenging task in Natural Language Processing (NLP).  ...  In this paper, we propose Cascaded multi-Structure Neural Network (CSNN), a novel and unified model that extract intersentence or implicit causal relations from Chinese Corpus, without relying on external  ...  The research reported in this paper is supported in part by the National Natural Science Foundation of China under the grant No. 91746203, 61991415, 61625304 and the Ant Financial Services Group.  ... 
doi:10.1007/978-3-030-47426-3_57 fatcat:zwplxjcvszg5bmzdq2wk6fmmeu

Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha
2018 Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications  
In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task.  ...  features associated within a text document along with a hierarchical convolution recurrent neural network framework.  ...  Deep neural network models, however, do not allow us to identify and extract those properties of text that the network identi-fies as discriminative (Alikaniotis et al., 2016) .  ... 
doi:10.18653/v1/w18-3713 dblp:conf/acl-tea/DasguptaNDS18 fatcat:ua5xzitnkre4patbvbxo5jo6gi

Relation extraction for coal mine safety information using recurrent neural networks with bidirectional minimal gated unit

Xiulei Liu, Shoulu Hou, Zhihui Qin, Sihan Liu, Jian Zhang
2021 EURASIP Journal on Wireless Communications and Networking  
This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model.  ...  It is of practical importance to extract information from big data to achieve disaster precaution and emergency response.  ...  [25] proposed an attention guided graph neural network for relation extraction tasks, which automatically learns how to selectively attend to the relevant sub-structures.  ... 
doi:10.1186/s13638-021-01936-0 fatcat:h2cipzwkcrbwhczydwymvspy6m

Natural Language Processing for Smart Healthcare [article]

Binggui Zhou, Guanghua Yang, Zheng Shi, Shaodan Ma
2022 arXiv   pre-print
We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view.  ...  In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application.  ...  With the early application of the neural probabilistic language model [3] and the rapid development of deep learning since 2013, neural NLP, by using neural networks and large corpora for automated feature  ... 
arXiv:2110.15803v2 fatcat:3o6tx5wqezdb5mgyfub2jtuiqu

Extracting event and their relations from texts: A survey on recent research progress and challenges

Kang Liu, Yubo Chen, Jian Liu, Xinyu Zuo, Jun Zhao
2020 AI Open  
How to identify events from texts, extract their arguments, even analyze the relations between different events are important for many applications.  ...  In event relation extraction, we focus on the extraction approaches for three typical event relation types, including coreference, causal and temporal relations, respectively.  ...  Priority Research Program of Chinese Academy of Sciences (Grant No.  ... 
doi:10.1016/j.aiopen.2021.02.004 fatcat:qxbcmk55vzcb5nznhgfgwrbe4u

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations  ...  Thus through this survey we can say that, NLP methods used to extract information ,brings new insights into already known clinical evidences.  ...  Recurrent Neural Networks text classification was used for it.  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq

Evolution of Techniques for Question Answering over Knowledge Base: A Survey

Ashish Salunkhe
2020 International Journal of Computer Applications  
The aim is to cover a concise yet complete understanding of the advances in Question Answering Systems classified based on domain and question type and brief information about metrics used to evaluate  ...  In this paper, a brief study of the advancements in the Question Answering domain as a type of information retrieval system is presented.  ...  Archana Chaugule, Head, Department of Computer Engineering, Pimpri Chinchwad College of Engineering and Research, for her encouragement and useful critiques for this research work.  ... 
doi:10.5120/ijca2020919817 fatcat:ywdhsihmefd2nczjeenljc2jym

A Multi-level Neural Network for Implicit Causality Detection in Web Texts [article]

Shining Liang, Wanli Zuo, Zhenkun Shi, Sen Wang, Junhu Wang, Xianglin Zuo
2021 arXiv   pre-print
To the best of our knowledge, with regards to the causality tasks, this is the first time that the Relation Network is applied.  ...  Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition.  ...  A tool automatically extracts meaningful causal relations could help us construct causality graphs [5] to unveil previously unknown relationships between events and accelerate the discovery of the intrinsic  ... 
arXiv:1908.07822v4 fatcat:2osc5g5ha5fbrnezmyao6e7hda

Generating Summaries for Scientific Paper Review [article]

Ana Sabina Uban, Cornelia Caragea
2021 arXiv   pre-print
We evaluate state of the art neural summarization models, present initial results on the feasibility of automatic review summary generation, and propose directions for the future.  ...  In order to test this hypothesis, we release a new dataset of scientific papers and their reviews, collected from papers published in the NeurIPS conference from 2013 to 2020.  ...  To obtain the full text of the papers, we downloaded the PDFs from the website and extracted the text using Grobid. 2 Reviews were extracted directly from the HTML content of the web pages, and, where  ... 
arXiv:2109.14059v1 fatcat:lg5ji2witrd2fdgumkjiw5oqum

Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding [article]

Rujun Han, Mengyue Liang, Bashar Alhafni, Nanyun Peng
2019 arXiv   pre-print
We demonstrate that neural network-based models can outperform some strong traditional linguistic feature-based models.  ...  We also conduct comparative studies to show the contribution of adopting contextualized word embeddings (BERT) for event temporal relation extraction from stories.  ...  This work is supported by Contract W911NF-15-1-0543 with the US Defense Advanced Research Projects Agency (DARPA).  ... 
arXiv:1904.11942v1 fatcat:oufg4z5amvhhljlkmgm2a2soqm

Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification

Zaili Chen, Kai Huang, Li Wu, Zhenyu Zhong, Zeyu Jiao
2022 Applied Sciences  
To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT.  ...  However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers  ...  Informed Consent Statement: Not applicable. Acknowledgments: All authors extend their sincerest thanks to the reviewers.  ... 
doi:10.3390/app12052482 fatcat:rl664t7xl5chjihbgvd4g5hmbi
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