| other,24-2-J05-1003,ak | initial <term> ranking </term> of these <term> | parses | </term> . A second <term> model </term> then | #8052 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses. | |
| other,45-4-J05-1003,ak | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #8120 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes thesefeatures into account. | |
| model,25-11-J05-1003,ak | feature selection methods </term> within <term> | log-linear ( maximum-entropy ) models | </term> . Although the experiments in this | #8295 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods withinlog-linear ( maximum-entropy ) models. |
