| tech,1-2-J05-1003,ak | <term> probabilistic parser </term> . The <term> | base parser | </term> produces a set of <term> candidate | #8029 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. | |
| other,11-2-J05-1003,ak | <term> candidate parses </term> for each <term> | input sentence | </term> , with associated <term> probabilities | #8039 The base parser produces a set of candidate parses for each input sentence , with associated probabilities that define an initial ranking of these parses. | |
| other,7-5-J05-1003,ak | We introduce a new method for the <term> | reranking task | </term> , based on the <term> boosting approach | #8131 We introduce a new method for the reranking task , based on the boosting approach to ranking problems described in Freund et al. (1998). | |
| measure(ment),14-8-J05-1003,ak | </term> , a 13 % relative decrease in <term> | F-measure error | </term> over the <term> baseline model ’s </term> | #8214 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. | |
| other,19-9-J05-1003,ak | of the <term> sparsity </term> of the <term> | feature space | </term> in the <term> parsing data </term> . | #8245 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| tech,36-12-J05-1003,ak | ranking tasks </term> , for example , <term> | speech recognition | </term> , <term> machine translation </term> | #8337 Although the experiments in this article are on natural 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,39-12-J05-1003,ak | , <term> speech recognition </term> , <term> | machine translation | </term> , or <term> natural language generation | #8340 Although the experiments in this article are on natural 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. |
