Results 11 to 20 of about 39,390 (254)
Frontiers in Computational Neuroscience, 2014 Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length $tau$, emph{i.e.}, the GC ...
Douglas eZhou +5 more
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Inter-sentence and Implicit Causality Extraction from Chinese Corpus [PDF]
Lecture Notes in Computer Science, 2020 Xinzhi Wang +2 more
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Event Causality Extraction Based on Two-Layer CNN-BiGRU-CRF [PDF]
Jisuanji gongcheng, 2021 The existing event causality extraction methods have poor performance in relationship boundary recognition and are limited by insufficient semantic representation of texts.
ZHENG Qiaoduo, WU Zhendong, ZOU Junying
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Implicit Causality Extraction Method Based on Event Action Direction [PDF]
Jisuanji kexue, 2022 Extracting the causality between events can be applied to automatic question answering,knowledge extraction,common sense reasoning and so on.Due to the lack of obvious lexical features and the complex syntactic structure of Chinese,it is very difficult ...
MIU Feng, WANG Ping, LI Tai-yong
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Extracting Money from Causal Decision Theorists [PDF]
The Philosophical Quarterly, 2021 Abstract Newcomb’s problem has spawned a debate about which variant of expected utility maximisation (if any) should guide rational choice. In this paper, we provide a new argument against what is probably the most popular variant: causal decision theory (CDT). In particular, we provide two scenarios in which CDT voluntarily loses money.
Oesterheld, C, Conitzer, V
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Information, 2023 This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by its relevance to different stakeholder group benefits (customers ...
Seethalakshmi Gopalakrishnan +5 more
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Towards Causality Extraction from Requirements [PDF]
2020 IEEE 28th International Requirements Engineering Conference (RE), 2020 System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements.
Jannik Fischbach +4 more
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Causal Features Extraction for Workpiece [PDF]
Journal of Physics: Conference Series, 2021 Abstract In order to reduce the cost, computer vision technology is introduced into the measurement of workpiece size and shape on the factory production line. At present, the most widely used solution is the neural network model based on big data.
Li Liu, Chen Xiang Huang
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Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [PDF]
Jisuanji kexue, 2022 The financial field has a large amount of information and high value,especially the implicit causal events which contains huge potential useful value.Carrying out causal analysis on financial domain text to mine the important information hidden in the ...
JIN Fang-yan, WANG Xiu-li
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Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
Applied Sciences, 2021 The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from ...
Wajid Ali +4 more
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