| Conclusions and future work | In a sense this is encouraging, as it motivates our most exciting future work: augmenting this simple model to explicitly capture complementary information such as distributional semantics (Blei et al., 2003), diathesis alternations (McCarthy, 2000) and selectional preferences (0 Séaghdha, 2010). |
| Conclusions and future work | By combining the syntactic classes with unsupervised POS tagging (Teichert and Daumé III, 2009) and the selectional preferences with distributional semantics (O Séaghdha, 2010), we hope to produce more accurate results on these complementary tasks while avoiding the use of any supervised learning. |
| Previous work | Graphical models have been increasingly popular for a variety of tasks such as distributional semantics (Blei et al., 2003) and unsupervised POS tagging (Finkel et al., 2007), and sampling methods allow efficient estimation of full joint distributions (Neal, 1993). |