Abstract
Most work on language acquisition treats word segmentation-the identification of linguistic segments from continuous speech- and word learning-the mapping of those segments to meanings-as separate problems. These two abilities develop in parallel, however, raising the question of whether they might interact. To explore the question, we present a new Bayesian segmentation model that incorporates aspects of word learning and compare it to a model that ignores word meanings. The model that learns word meanings proposes more adult-like segmentations for the meaning-bearing words. This result suggests that the non-linguistic context may supply important information for learning word segmentations as well as word meanings.
| Original language | English |
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| Title of host publication | NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference |
| Place of Publication | Stroudsburg, PA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 501-509 |
| Number of pages | 9 |
| ISBN (Print) | 1932432655, 9781932432657 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010 - Los Angeles, CA, United States Duration: 2 Jun 2010 → 4 Jun 2010 |
Other
| Other | 2010 Human Language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010 |
|---|---|
| Country/Territory | United States |
| City | Los Angeles, CA |
| Period | 2/06/10 → 4/06/10 |