Results 221 to 230 of about 39,390 (254)
Some of the next articles are maybe not open access.
Event causality extraction based on connectives analysis
Neurocomputing, 2016Causality is an important type of relation which is crucial in numerous tasks, such as predicting future events, generating scenario, question answering, textual entailment and discourse comprehension. Therefore, causality extraction is a fundamental task in text mining.
Sicheng Zhao +2 more
exaly +2 more sources
Causality Extraction in Futures Domain
2021 International Conference on Asian Language Processing (IALP), 2021Yuxiang Jia, Hongying Zan
exaly +2 more sources
A survey of the extraction and applications of causal relations
Natural Language Engineering, 2022AbstractCausationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal ...
Brett Drury +2 more
openaire +1 more source
Chinese causal event extraction using causality‐associated graph neural network
Concurrency and Computation: Practice and Experience, 2021AbstractCausal event extraction (CEE) aims to identify and extract cause‐effect event pairs from texts, which is a fundamental task in natural language processing. Recent research treat CEE as a sequence labeling problem. However, the linguistic complexity and ambiguity of textual description results in the low accuracy of extractors.
Jianqi Gao 0001 +2 more
openaire +1 more source
Causal Orthogonal Functions: A Causal Inference approach to temporal feature extraction
2022<p>Understanding complex dynamical systems is a major challenge in many scientific disciplines. There are two aspects which are of particular interest when analyzing complex dynamical systems: 1) the temporal patterns along which they evolve and 2) the governing causal mechanisms.</p><p>Temporal patterns in a ...
Nicolas-Domenic Reiter +2 more
openaire +1 more source
Extracting Causal Nets from Databases
2007Causal nets (Pearl 1986) are an elegant way of representing the structure and relationships of a set of data. The propagation of changes through the net has been examined and reported on in many works (Pearl 1986, Lauritzen & Speigelhalter 1988, Neapolitan 1990).
openaire +1 more source
Extracting a Causal Network of News Topics
2012Because of the abundance of online news, it is impossible for users to process all the available information. Tools are needed to help process this information. To mitigate this challenge we propose generating a network of causally related news topics to help the user understand and navigate throughout the news.
openaire +1 more source
Extracting causal time domain parameters
2004 10th International Symposium on Antenna Technology and Applied Electromagnetics and URSI Conference, 2004Parameters of a device are normally given in a frequency range of interest. To use them in a time-domain simulator requires their time-domain correspondents to be strictly causal in time. However, these time-domain correspondents are often non-causal in time if they are obtained from simple transformations of the frequency-domain parameters known over ...
Shuiping Luo, Zhizhang Chen
openaire +1 more source
Causal Pattern Representation Learning for Extracting Causality from Literature
Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing, 2022Jiaoyun Yang +4 more
openaire +1 more source