| Abstract | We show that this constraint is effective on the sentiment classification task (Fang et al., 2002), resulting in scores similar to the ones obtained by the structural correspondence methods (Blitzer et al., 2007) without the need to engineer auxiliary tasks. |
| Constraints on Inter-Domain Variability | At least some of these clusters, when induced by maximizing the likelihood L(6, a) with sufficiently large 04, will be useful for the classification task on the source domain. |
| Discussion and Conclusions | Our approach results in competitive domain-adaptation performance on the sentiment classification task , rivalling that of the state-of-the-art SCL method (Blitzer et al., 2007). |
| Empirical Evaluation | In this section we empirically evaluate our approach on the sentiment classification task . |
| Empirical Evaluation | On the sentiment classification task in order to construct them two steps need to be performed: (1) a set of words correlated with the sentiment label is selected, and, then (2) prediction of each such word is regarded a distinct auxiliary problem. |
| The Latent Variable Model | In this paper we consider classification tasks , namely prediction of sentiment polarity of a user review (Pang et al., 2002), and model the joint distribution of the binary sentiment label 3/ E {0, l} and the multiset of text features :13, :ci 6 X. |
| The Latent Variable Model | Consequently, the latent representation induced in this way is likely to be inappropriate for the classification task in question. |