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Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks

Canasai Kruengkrai, Kentaro Torisawa, Chikara Hashimoto, Julien Kloetzer, Jong-Hoon Oh, Masahiro Tanaka
We retrieve texts related to our event causality candidates from four billion web pages by three distinct methods, including a why-question answering system, and feed them to our multi-column convolutional  ...  This allows us to identify the useful background knowledge scattered in web texts and effectively exploit the identified knowledge to recognize event causalities.  ...  Oh et al. (2017) proposed the most similar framework to ours; they used a variant of a multi-column convolutional neural network for why-QA and gave additional texts to columns as background knowledge  ... 
doi:10.1609/aaai.v31i1.11005 fatcat:2wjoi67h3fe3nj3nmacr6uxpsy

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  
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  ...  Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers.  ...  [174] propose a novel technique using multi-column convolutional neural networks (MCNNs) and source background knowledge (BK) for CM.  ... 
doi:10.3390/app112110064 fatcat:btv66da5x5a73auogv5d3lp2bi

A Survey of Implicit Discourse Relation Recognition [article]

Wei Xiang, Bang Wang
2022 arXiv   pre-print
We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history.  ...  A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions.  ...  Illustration of using a convolution neural network for implicit discourse relation recognition. Fig. 7 . 7 Fig. 7.  ... 
arXiv:2203.02982v1 fatcat:ubublxw2fnfdpexgw4jslj76tm

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.  ...  Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and develop the SCRN to infer causality at segment level.  ...  For neural model based methods, Oh et al. [3] proposed a multi-column convolutional neural network with causality-attention (CA-MCNN) to enhance MCNNs with the causality-attention. Zhao et al.  ... 
arXiv:1908.07822v4 fatcat:2osc5g5ha5fbrnezmyao6e7hda

BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation [article]

Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yiting Dong, Enmeng Lu, Qian Zhang, Yinqian Sun, Qian Liang, Yuxuan Zhao, Zhuoya Zhao, Hongjian Fang (+7 others)
2022 arXiv   pre-print
brain-inspired spiking neural network based AI, and to simulate the cognitive brains at multiple scales.  ...  These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired  ...  Spiking neural network with time differential convolution kernel (TCK) is used to do further classification shown in Fig. 8 .  ... 
arXiv:2207.08533v1 fatcat:pb2ah43qlra7zmvhr4no27ovcu

Survey on the Application of Deep Learning in Extreme Weather Prediction

Wei Fang, Qiongying Xue, Liang Shen, Victor S. Sheng
2021 Atmosphere  
These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather.  ...  Extreme weather events can be called high-impact weather, the 'extreme' here means that the probability of occurrence is very small.  ...  Two state-of-the-art deep learning techniques are used for pattern recognition: convolutional neural network and more advanced capsule neural network.  ... 
doi:10.3390/atmos12060661 fatcat:viaddv3vvfa6vfzxobybtcdgvy

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence.  ...  data such as social media, news sources, blogs, economic indicators, and other meta-data sources.  ...  Early Model Based Event Recognition using Surrogates (EMBERS) [90, 102] is an automated system developed for generating forecasts about civil unrest from massive and multiple data sources.  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

A Survey of Sound Source Localization with Deep Learning Methods [article]

Pierre-Amaury Grumiaux, Srđan Kitić, Laurent Girin, Alexandre Guérin
2022 arXiv   pre-print
This article is a survey on deep learning methods for single and multiple sound source localization.  ...  We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the  ...  They also used a couple of 1D causal convolutional layers at the end of the network to perform single-source tracking.  ... 
arXiv:2109.03465v3 fatcat:tq5vmgikwrenlbqba4lrqo3pee

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
value -Improving reward accuracy by associating multiple sensors using Hebb's rule- CICA2: Neural Network Control/Fuzzy Systems and Control/Intelligent and AI Based Control, Chair: Xiao-Jun Zeng Daoyi  ...  Mormille and Masayasu Atsumi .......... 2670 Evolving Optimal Convolutional Neural Networks Subhashis Banerjee and Sushmita Mitra .......... 2677 GPCNN: Evolving Convolutional Neural Networks using Genetic  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li

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.  ...  Lastly, we highlight existing open challenges with their potential directions.  ...  Within this model, different columns represent different inputs, such as event causality candidates, contextual information, and background knowledge, with each column having its independent convolutional  ... 
arXiv:2101.06426v2 fatcat:hd3ikb7mejcndlq6wsgojv4uoa

Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment

Hao Nie, Xianpei Han, Le Sun, Chi Man Wong, Qiang Chen, Suhui Wu, Wei Zhang
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit  ...  Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object.  ...  Introduction Causal knowledge acquisition is crucial for various Artificial Intelligence tasks, such as causal event graph construction, reading comprehension and future event prediction.  ... 
doi:10.24963/ijcai.2020/502 dblp:conf/ijcai/LiD0HD20 fatcat:5s7fiimqabdsbexafjjm75sbgu

Review on data analysis methods for mesoscale neural imaging in vivo

Yeyi Cai, Jiamin Wu, Qionghai Dai
2022 Neurophotonics  
Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely  ...  The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction.  ...  But there are still multiple challenges for existing methods to deal with the complex network of the brain.  ... 
doi:10.1117/1.nph.9.4.041407 pmid:35450225 pmcid:PMC9010663 fatcat:yalq65vasrgzbf3gwweu5vkrcu

Deep Learning Methods for Human Behavior Recognition

Jia Lu, Minh Nguyen, Wei Qi Yan
2020 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)  
Hence, it is necessary to develop the methods of real-time human behavior recognition so as to reduce security staff's workload and improve work efficiency.  ...  We proposed Selective Kernel Network (SKNet) and ResNeXt with attention mechanism, which generate positive results to recognize human behaviours.  ...  CNN links the upper and lower layers through convolution kernels. A convolutional neural network is a deep neural network with a convolutional structure.  ... 
doi:10.1109/ivcnz51579.2020.9290640 fatcat:sq4fni6z2nfz5okecnsbmzum6e

The artificial intelligence renaissance: deep learning and the road to human-Level machine intelligence

Kar-Han Tan, Boon Pang Lim
2018 APSIPA Transactions on Signal and Information Processing  
A number of problems that were considered too challenging just a few years ago can now be solved convincingly by deep neural networks.  ...  network architectures.  ...  ACKNOWLEDGEMENTS The first author would like to thank Irwin Sobel for pointers on the pioneering work at MIT, and Xiaonan Zhou for her work on many of the deep neural network results shown.  ... 
doi:10.1017/atsip.2018.6 fatcat:6iftrepekjdmjffcb5ouz42jke

Review of end-to-end speech synthesis technology based on deep learning [article]

Zhaoxi Mu, Xinyu Yang, Yizhuo Dong
2021 arXiv   pre-print
Moreover, this paper also summarizes the open-source speech corpus of English, Chinese and other languages that can be used for speech synthesis tasks, and introduces some commonly used subjective and  ...  Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414-2423 56.  ...  Non-causal CNN is used on width dimension, causal CNN with autoregressive constraints is used on height dimension, and convolution queue [153] is used to cache the intermediate hidden states to speed  ... 
arXiv:2104.09995v1 fatcat:q5lx74ycx5hobjox4ktl3amfta
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