| measure(ment),13-3-C92-1055,ak | <term> parameters </term> to maximize the <term> | accuracy rate | </term> directly . To make the proposed <term> | #22847 The proposed method remedies these problems by adjusting the parameters to maximize theaccuracy rate directly. | |
| measure(ment),1-6-C92-1055,ak | has been observed in the test . The <term> | accuracy rate | </term> of <term> syntactic disambiguation </term> | #22898 Theaccuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach. | |
| tech,4-4-C92-1055,ak | </term> directly . To make the proposed <term> | algorithm | </term> robust , the possible variations | #22855 To make the proposedalgorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members. | |
| other,9-2-C92-1055,ak | insufficient <term> training data </term> and <term> | approximation error | </term> introduced by the <term> language model | #22801 Owing to the problem of insufficient training data andapproximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. | |
| other,31-4-C92-1055,ak | separation margin </term> between the correct <term> | candidate | </term> and its competing members . Significant | #22882 To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correctcandidate and its competing members. | |
| tech,5-1-C92-1055,ak | resolution </term> . In this paper , a <term> | discrimination and robustness oriented adaptive learning procedure | </term> is proposed to deal with the task | #22773 In this paper, adiscrimination and robustness oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution. | |
| model,14-2-C92-1055,ak | approximation error </term> introduced by the <term> | language model | </term> , traditional <term> statistical approaches | #22806 Owing to the problem of insufficient training data and approximation error introduced by thelanguage model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. | |
| tech,29-2-C92-1055,ak | by indirectly and implicitly using <term> | maximum likelihood method | </term> , fail to achieve high performance | #22821 Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly usingmaximum likelihood method, fail to achieve high performance in real applications. | |
| other,9-3-C92-1055,ak | remedies these problems by adjusting the <term> | parameters | </term> to maximize the <term> accuracy rate | #22843 The proposed method remedies these problems by adjusting theparameters to maximize the accuracy rate directly. | |
| other,26-4-C92-1055,ak | into consideration by enlarging the <term> | separation margin | </term> between the correct <term> candidate | #22877 To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging theseparation margin between the correct candidate and its competing members. | |
| tech,18-2-C92-1055,ak | language model </term> , traditional <term> | statistical approaches | </term> , which resolve <term> ambiguities </term> | #22810 Owing to the problem of insufficient training data and approximation error introduced by the language model, traditionalstatistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. | |
| tech,20-1-C92-1055,ak | proposed to deal with the task of <term> | syntactic ambiguity resolution | </term> . Owing to the problem of insufficient | #22788 In this paper, a discrimination and robustness oriented adaptive learning procedure is proposed to deal with the task ofsyntactic ambiguity resolution. | |
| tech,4-6-C92-1055,ak | . The <term> accuracy rate </term> of <term> | syntactic disambiguation | </term> is raised from 46.0 % to 60.62 % | #22901 The accuracy rate ofsyntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach. | |
| lr,12-4-C92-1055,ak | the possible variations between the <term> | training corpus | </term> and the real tasks are also taken | #22863 To make the proposed algorithm robust, the possible variations between thetraining corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members. | |
| lr,6-2-C92-1055,ak | Owing to the problem of insufficient <term> | training data | </term> and <term> approximation error </term> | #22798 Owing to the problem of insufficienttraining data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. |
