Automatic extraction of cause-effect relations in Natural Language Text Antonio Sorgente, Giuseppe Vettigli, and Francesco Mele ... adopting various approaches based on rules or Machine Learning (ML) ... to combine the advantage of each one. In particular, our approach first identifies a set of plausible cause-effect pairs through a set of ...

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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction 53 spatial locality in their latent higher-level feature representations.

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Information extraction ... list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in …

Combine: Combine, a complex farm machine that both cuts and threshes grain. Combines were not generally adopted until the 1930s, when tractor-drawn models became available. Self-propelled machines appeared a decade later.

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The difficulties of road extraction from RS images lie in that the image characteristics of road features can be affected by the sensor type, spectral and spatial resolution, weather, light variation, and ground characteristic, etc.

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74 While solid/liquid extraction is the most common technique used to brew beverages and isolate natural products, liquid/liquid extraction is a very common method used in the organic laboratory.

Elementary: Large-scale Knowledge-base Construction via Machine Learning and Statistical Inference Feng Niu, Ce Zhang, Christopher Ré, and Jude Shavlik ... extraction, machine learning, statistical inference, systems Introduction Knowledge-base construction (KBC) is the process of ... combine diverse data resources and best-of-breed algorithms

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Liquid-Liquid Extraction Equipment Jack D. Law and Terry A. Todd Idaho National Laboratory . Liquid-liquid extraction (also called solvent extraction) was initially utilized in the petroleum industry beginning in the 1930’s. It has since been utilized in numerous applications including

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Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is required to quickly complete tasks such as image matching and ...

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The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. Note Feature extraction is very different from Feature selection: the former consists in transforming arbitrary data ...