Estimation of principal points for a multivariate binary distribution using a log-linear model

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

Download Full-text

Application of log-linear model in inference on karyotypic evolution in chronic myelocytic leukemia.

Environmental Health Perspectives ◽

10.1289/ehp.9087135 ◽

1990 ◽

Vol 87 ◽

pp. 135-141 ◽

Cited By ~ 2

Author(s):

T Hashimoto ◽

M Ohtaki ◽

N Kamada ◽

H Yamamoto ◽

M Munaka

Keyword(s):

Linear Model ◽

Chronic Myelocytic Leukemia ◽

Karyotypic Evolution ◽

Myelocytic Leukemia ◽

Log Linear

Download Full-text

The Application of Log-Linear Model to Selected Poison Patients

ASM Science Journal ◽

10.32802/asmscj.2020.sm26(1.21) ◽

2020 ◽

pp. 1-7

Author(s):

Fatin N.S.A. ◽

Norlida M.N. ◽

Siti Z.M.J.

Keyword(s):

Linear Model ◽

Linear Models ◽

Demographic Data ◽

Contingency Tables ◽

Categorical Variables ◽

State Variables ◽

Exposure Route ◽

Higher Dimensional ◽

Route Of Exposure ◽

Log Linear

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

Download Full-text

Scrapie-resistant sheep show certain coat colour characteristics

Genetics Research ◽

10.1017/s0016672308009968 ◽

2009 ◽

Vol 91 (1) ◽

pp. 39-46 ◽

Cited By ~ 1

Author(s):

R. M. SAWALHA ◽

L. BELL ◽

S. BROTHERSTONE ◽

I. WHITE ◽

A. J. WILSON ◽

...

Keyword(s):

Maximum Likelihood ◽

Linear Model ◽

Morphological Diversity ◽

Coat Colour ◽

Unintended Consequences ◽

Colour Pattern ◽

Reverse Pattern ◽

Colour Patterns ◽

Log Linear ◽

The Relationship

SummarySusceptibility to scrapie is known to be associated with polymorphisms at the prion protein (PrP) gene, and this association is the basis of current selective programmes implemented to control scrapie in many countries. However, these programmes might have unintended consequences for other traits that might be associated withPrPgenotype. The objective of this study was to investigate the relationship betweenPrPgenotype and coat colour characteristics in two UK native sheep breeds valued for their distinctive coat colour patterns. Coat colour pattern, darkness and spotting andPrPgenotype records were available for 11 674 Badgerfaced Welsh Mountain and 2338 Shetland sheep. The data were analysed with a log–linear model using maximum likelihood. Results showed a strong significant association ofPrPgenotype with coat colour pattern in Badgerfaced Welsh Mountain and Shetland sheep and with the presence of white spotting in Shetland sheep. Animals with the ARR/ARR genotype (the most scrapie resistant) had higher odds of having a light dorsum and a dark abdomen than the reverse pattern. The implication of these associations is that selection to increase resistance to scrapie based only onPrPgenotype could result in change in morphological diversity and affect other associated traits such as fitness.

Download Full-text