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Academic Paper

Title: Generating example contexts to help children learn word meaning
Author: Liu Liu
Institution: Google Pittsburgh
Author: Jack Mostow
Institution: Carnegie Mellon University
Author: Gregory S. Aist
Email: click here TO access email
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.


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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