| tech,23-12-J05-1003,ak | should be applicable to many other <term> | NLP problems | </term> which are naturally framed as <term> | #8324 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many otherNLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. | |
| tech,9-9-J05-1003,ak | a new <term> algorithm </term> for the <term> | boosting approach | </term> which takes advantage of the <term> | #8235 The article also introduces a new algorithm for theboosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| model,40-4-J05-1003,ak | define a <term> derivation </term> or a <term> | generative model | </term> which takes these <term> features </term> | #8115 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 agenerative model which takes these features into account. | |
| tech,21-11-J05-1003,ak | simplicity and efficiency — to work on <term> | feature selection methods | </term> within <term> log-linear ( maximum-entropy | #8291 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models. |
