Publishing Partner: Cambridge University Press CUP Extra Wiley-Blackwell Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

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.


New from Cambridge University Press!

ad

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.


New from Brill!

ad

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: Active learning and logarithmic opinion pools for HPSG parse selection
Author: Jason Baldridge
Institution: University of Texas at Austin
Author: Miles Osborne
Institution: University of Edinburgh
Linguistic Field: Computational Linguistics
Abstract: For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material must therefore be considered. We investigate the relationship between two broad strategies for reducing the amount of manual labelling necessary to train accurate parse selection models: ensemble models and active learning. We show that popular active learning methods for reducing annotation costs can be outperformed by instead using a model class which uses the available labelled data more efficiently. For this, we use a simple type of ensemble model called the (LOP). We furthermore show that LOPs themselves can benefit from active learning. As predicted by a theoretical explanation of the predictive power of LOPs, a detailed analysis of active learning using LOPs shows that component model diversity is a strong predictor of successful LOP performance. Other contributions include a novel active learning method, a justification of our simulation studies using timing information, and cross-domain verification of our main ideas using text classification.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 14, Issue 2, which you can read on Cambridge's site or on LINGUIST .



Back
Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page