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


New from Oxford University Press!

ad

Language Planning as a Sociolinguistic Experiment

By: Ernst Jahr

Provides richly detailed insight into the uniqueness of the Norwegian language development. Marks the 200th anniversary of the birth of the Norwegian nation following centuries of Danish rule


New from Cambridge University Press!

ad

Acquiring Phonology: A Cross-Generational Case-Study

By Neil Smith

The study also highlights the constructs of current linguistic theory, arguing for distinctive features and the notion 'onset' and against some of the claims of Optimality Theory and Usage-based accounts.


New from Brill!

ad

Language Production and Interpretation: Linguistics meets Cognition

By Henk Zeevat

The importance of Henk Zeevat's new monograph cannot be overstated. [...] I recommend it to anyone who combines interests in language, logic, and computation [...]. David Beaver, University of Texas at Austin


Academic Paper


Title: Active learning and logarithmic opinion pools for HPSG parse selection
Author: JasonBaldridge
Institution: University of Texas at Austin
Author: MilesOsborne
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