| tech,6-9-J05-1003,ak | The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> . | #8232 The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| tech,8-10-J05-1003,ak | Experiments show significant efficiency gains for the new <term> algorithm </term> over the obvious implementation of the <term> boosting approach </term> . | #8260 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. | |
| tech,8-12-J05-1003,ak | Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the approach should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </term> . | #8309 Although the experiments in this article are onnatural language parsing ( NLP ), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. | |
| tech,9-9-J05-1003,ak | The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </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. |
