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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


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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.


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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: 'Generating example contexts to help children learn word meaning'
Author: LiuLiu
Institution: 'Google Pittsburgh'
Author: JackMostow
Institution: 'Carnegie Mellon University'
Author: GregoryS.Aist
Email: click here to access email
Homepage: 'http://www.gregoryaist.com'
Linguistic Field: 'Computational Linguistics'
Abstract: This article addresses the problem of generating good example contexts to help children learn vocabulary. We describe VEGEMATIC, a system that constructs such contexts by concatenating overlapping five-grams from Google's N-gram corpus. We propose and operationalize a set of constraints to identify good contexts. VEGEMATIC uses these constraints to filter, cluster, score, and select example contexts. An evaluation experiment compared the resulting contexts against human-authored example contexts (e.g., from children's dictionaries and children's stories). Based on rating by an expert blind to source, their average quality was comparable to story sentences, though not as good as dictionary examples. A second experiment measured the percentage of generated contexts rated by lay judges as acceptable, and how long it took to rate them. They accepted only 28% of the examples, but averaged only 27 seconds to find the first acceptable example for each target word. This result suggests that hand-vetting VEGEMATIC's output may supply example contexts faster than creating them manually.

CUP at LINGUIST

This article appears in Natural Language Engineering Vol. 19, Issue 2, which you can read on Cambridge's site or on LINGUIST .



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