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Latin: A Linguistic Introduction

By Renato Oniga and Norma Shifano

Applies the principles of contemporary linguistics to the study of Latin and provides clear explanations of grammatical rules alongside diagrams to illustrate complex structures.


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The Ancient Language, and the Dialect of Cornwall, with an Enlarged Glossary of Cornish Provincial Words

By Frederick W.P. Jago

Containing around 3,700 dialect words from both Cornish and English,, this glossary was published in 1882 by Frederick W. P. Jago (1817–92) in an effort to describe and preserve the dialect as it too declined and it is an invaluable record of a disappearing dialect and way of life.


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Linguistic Bibliography for the Year 2013

The Linguistic Bibliography is by far the most comprehensive bibliographic reference work in the field. This volume contains up-to-date and extensive indexes of names, languages, and subjects.


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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