Maximum Entropy Principle based Document Ranking with Term Selection Analysis for Cross-Lingual Information Retrieval

2021 ◽  
Author(s):  
Ms. P. Mahalakshmi ◽  
N. Sabiyath Fatima
Keyword(s):  
Information Retrieval ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Document Ranking ◽  
Term Selection ◽  
Selection Analysis ◽  
Entropy Principle ◽  
Cross Lingual

A TREATMENT OF USEFULNESS OF KEYWORDS IN FUZZY REQUESTS FOR AN INFORMATION RETRIEVAL SYSTEM WITH BAYESIAN NETWORKS

1999 ◽  
Vol 07 (04) ◽  
pp. 399-406
Author(s):  
KENJI SAITO ◽  
HIROYUKI SHIOYA ◽  
TSUTOMU DA-TE
Keyword(s):  
Information Retrieval ◽  
Bayesian Network ◽  
Maximum Entropy ◽  
Probabilistic Model ◽  
Retrieval System ◽  
Maximum Entropy Principle ◽  
Document Retrieval ◽  
Information Retrieval System ◽  
Retrieval Method ◽  
Entropy Principle

We improve a document retrieval method based on the so-called maximum entropy principle proposed by Cooper, and show how to implement this system on a Bayesian network. A Bayesian network is a probabilistic model for expressing probabilistic relations among random variables. We show advantages of a document retrieval system on a Bayesian network in comparison with Cooper's system. The original document retrieval system based on the maximum entropy principle has a drawback: a result of retrieval can not be obtained in some cases. In this paper, we resolve this drawback by fuzzification of user retrieval requests.

An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert
Keyword(s):  
Artificial Intelligence ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Maximum Entropy Principle ◽  
Statistical Modelling ◽  
Linear Constraints ◽  
Descriptive Statistics ◽  
Entropy Principle ◽  
Maximum Entropy Distribution ◽  
Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

SINE ENTROPY FOR UNCERTAIN VARIABLES

2013 ◽  
Vol 21 (05) ◽  
pp. 743-753 ◽  
Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI
Keyword(s):  
Maximum Entropy ◽  
Uncertainty Theory ◽  
Maximum Entropy Principle ◽  
Expected Value ◽  
Uncertain Variables ◽  
Entropy Principle

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.

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