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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.<\/jats:p>","DOI":"10.1017\/s1351324911000374","type":"journal-article","created":{"date-parts":[[2012,1,12]],"date-time":"2012-01-12T10:03:26Z","timestamp":1326362606000},"page":"187-212","source":"Crossref","is-referenced-by-count":1,"title":["Generating example contexts to help children learn word meaning"],"prefix":"10.1017","volume":"19","author":[{"given":"LIU","family":"LIU","sequence":"first","affiliation":[]},{"given":"JACK","family":"MOSTOW","sequence":"additional","affiliation":[]},{"given":"GREGORY S.","family":"AIST","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2012,1,12]]},"reference":[{"key":"S1351324911000374_ref8","doi-asserted-by":"crossref","first-page":"819","DOI":"10.3115\/1220575.1220678","volume-title":"Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in 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