scholarly journals Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation

2007 ◽  
Vol 137 (11) ◽  
pp. 3430-3445 ◽  
Author(s):  
Stephen E. Fienberg ◽  
Alessandro Rinaldo
Keyword(s):  
Data Analysis ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Categorical Data ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Categorical Data Analysis ◽  
Log Linear

Extracting paraphrase patterns from bilingual parallel corpora

2009 ◽  
Vol 15 (4) ◽  
pp. 503-526 ◽  
Author(s):  
SHIQI ZHAO ◽  
HAIFENG WANG ◽  
TING LIU ◽  
SHENG LI
Keyword(s):  
Natural Language Processing ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Language Processing ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Word Alignment ◽  
Inference Rules ◽  
Parallel Corpora ◽  
Log Linear

AbstractParaphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented method, we extract more than 1 million pairs of paraphrase patterns from about 2 million pairs of bilingual parallel sentences. The precision of the extracted paraphrase patterns is above 78%. Experimental results show that the presented method significantly outperforms a well-known method called discovery of inference rules from text (DIRT). Additionally, the log-linear model with the proposed feature functions are effective. The extracted paraphrase patterns are fully analyzed. Especially, we found that the extracted paraphrase patterns can be classified into five types, which are useful in multiple natural language processing (NLP) applications.

scholarly journals Maximum likelihood estimation in log-linear models

2012 ◽  
Vol 40 (2) ◽  
pp. 996-1023 ◽  
Author(s):  
Stephen E. Fienberg ◽  
Alessandro Rinaldo
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Log Linear

scholarly journals Toric invariant theory for maximum likelihood estimation in log-linear models

2021 ◽  
Vol 12 (2) ◽  
pp. 187-211
Author(s):  
Carlos Améndola ◽  
Kathlén Kohn ◽  
Philipp Reichenbach ◽  
Anna Seigal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Invariant Theory ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Log Linear
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