Name
Abstract | ||
|---|---|---|
Motivated by recent work in phonotactic learning (Hayes and Wilson 2008, Albright 2009), this paper shows how to define feature-based probability distributions whose parameters can be provably efficiently estimated. The main idea is that these distributions are defined as a product of simpler distributions (cf. Ghahramani and Jordan 1997). One advantage of this framework is it draws attention to what is minimally necessary to describe and learn phonological feature interactions in phonotactic patterns. The "bottom-up" approach adopted here is contrasted with the "top-down" approach in Hayes and Wilson (2008), and it is argued that the bottom-up approach is more analytically transparent. |
Year | Venue | Keywords |
|---|---|---|
2010 | SIGMORPHON | main idea,maximum likelihood estimation,phonological feature interaction,recent work,simpler distribution,feature-based distribution,feature-based probability distribution,bottom-up approach,phonotactic pattern,phonotactic learning |
Field | DocType | Volume |
Phonotactics,Computer science,Maximum likelihood,Speech recognition,Probability distribution,Artificial intelligence,Natural language processing,Feature based,Machine learning | Conference | W10-22 |
Citations | PageRank | References |
1 | 0.48 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Heinz | 1 | 118 | 19.03 |
| Cesar Koirala | 2 | 6 | 1.61 |