Probabilistic semantics objectified: II. Implication in probabilistic model sets

1981 ◽  
Vol 10 (4) ◽  
pp. 495-510 ◽  
Author(s):  
Bas C. Van Fraassen
Keyword(s):  
Probabilistic Model ◽  
Model Sets ◽  
Probabilistic Semantics

Model sets with positive entropy in Euclidean cut and project schemes

2019 ◽  
Vol 52 (5) ◽  
pp. 1073-1106 ◽  
Author(s):  
Tobias JÄGER ◽  
Daniel LENZ ◽  
Christian OERTEL
Keyword(s):  
Positive Entropy ◽  
Model Sets

Assessment and Analysis of Credit Risk with Using of Logical and Probabilistic Model

2002 ◽  
Author(s):  
Vassilij Karassev ◽  
Andrey Roukine ◽  
E.D. Dmitrievich Solojentsev
Keyword(s):  
Credit Risk ◽  
Probabilistic Model

scholarly journals Joint Semantic Synthesis and Morphological Analysis of the Derived Word

2018 ◽  
Vol 6 ◽  
pp. 33-48 ◽  
Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Probabilistic Model ◽  
Morphological Analysis ◽  
Semantic Representation ◽  
English Word ◽  
Word Formation ◽  
Additive Models ◽  
Segmentation Accuracy ◽  
Morphological Productivity

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

A probabilistic model for the prediction of intra-abdominal infection after colorectal surgery

2021 ◽  
Author(s):  
Carmen Cagigas Fernández ◽  
Camilo Palazuelos ◽  
Lidia Cristobal Poch ◽  
Marcos Gomez Ruiz
Keyword(s):  
Colorectal Surgery ◽  
Probabilistic Model ◽  
Abdominal Infection ◽  
Intra Abdominal Infection
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