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Oxford Handbook of Corpus Phonology

Edited by Jacques Durand, Ulrike Gut, and Gjert Kristoffersen

Offers the first detailed examination of corpus phonology and serves as a practical guide for researchers interested in compiling or using phonological corpora


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The Languages of the Jews: A Sociolinguistic History

By Bernard Spolsky

A vivid commentary on Jewish survival and Jewish speech communities that will be enjoyed by the general reader, and is essential reading for students and researchers interested in the study of Middle Eastern languages, Jewish studies, and sociolinguistics.


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Indo-European Linguistics

New Open Access journal on Indo-European Linguistics is now available!


Academic Paper


Title: A cross-corpus study of subjectivity identification using unsupervised learning
Author: Dong Wang
Institution: University of Texas at Dallas
Author: Yang Liu
Institution: University of Texas at Dallas
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: In this study, we investigate using unsupervised generative learning methods for subjectivity detection across different domains. We create an initial training set using simple lexicon information and then evaluate two iterative learning methods with a base naive Bayes classifier to learn from unannotated data. The first method is self-training, which adds instances with high confidence into the training set in each iteration. The second is a calibrated EM (expectation-maximization) method where we calibrate the posterior probabilities from EM such that the class distribution is similar to that in the real data. We evaluate both approaches on three different domains: movie data, news resource, and meeting dialogues, and we found that in some cases the unsupervised learning methods can achieve performance close to the fully supervised setup. We perform a thorough analysis to examine factors, such as self-labeling accuracy of the initial training set in unsupervised learning, the accuracy of the added examples in self-training, and the size of the initial training set in different methods. Our experiments and analysis show inherent differences across domains and impacting factors explaining the model behaviors.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 18, Issue 3, which you can read on Cambridge's site .



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