Scaling Information-Theoretic Text Clustering: A Sampling-based Approximate Method

2014 ◽  
Author(s):  
Zhexi Xu ◽  
Zhiang Wu ◽  
Jie Cao ◽  
Hengnong Xuan
Keyword(s):  
Approximate Method ◽  
Text Clustering ◽  
Information Theoretic

SAIL: Summation-bAsed Incremental Learning for Information-Theoretic Text Clustering

2013 ◽  
Vol 43 (2) ◽  
pp. 570-584 ◽  
Author(s):  
Jie Cao ◽  
Zhiang Wu ◽  
Junjie Wu ◽  
Hui Xiong
Keyword(s):  
Incremental Learning ◽  
Text Clustering ◽  
Information Theoretic

Large-sample confidence intervals of information-theoretic measures in linguistics

2020 ◽  
Vol 6 (1) ◽  
pp. 19-54
Author(s):  
Ryan Ka Yau Lai ◽  
Youngah Do
Keyword(s):  
Maximum Likelihood ◽  
Corpus Linguistics ◽  
Delta Method ◽  
Confidence Bounds ◽  
Likelihood Estimator ◽  
Information Theoretic ◽  
Leibler Divergence ◽  
Information Theoretic Measures ◽  
Data Points ◽  
Measure Of Uncertainty

This article explores a method of creating confidence bounds for information-theoretic measures in linguistics, such as entropy, Kullback-Leibler Divergence (KLD), and mutual information. We show that a useful measure of uncertainty can be derived from simple statistical principles, namely the asymptotic distribution of the maximum likelihood estimator (MLE) and the delta method. Three case studies from phonology and corpus linguistics are used to demonstrate how to apply it and examine its robustness against common violations of its assumptions in linguistics, such as insufficient sample size and non-independence of data points.

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