Concordance

Query C92-1055 14 (652.7 per million)

tech,9-1-C92-1055,bq	In this paper , a discrimination and robustness oriented <term> adaptive learning procedure </term> is proposed to deal with the task of <term> syntactic ambiguity resolution </term> .	#17806 In this paper, a discrimination and robustness orientedadaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution.
other,20-1-C92-1055,bq	In this paper , a discrimination and robustness oriented <term> adaptive learning procedure </term> is proposed to deal with the task of <term> syntactic ambiguity resolution </term> .	#17817 In this paper, a discrimination and robustness oriented adaptive learning procedure is proposed to deal with the task ofsyntactic ambiguity resolution.
other,5-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17826 Owing to the problem ofinsufficient training 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.
other,9-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17830 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.
model,14-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17835 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,18-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17839 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.
other,23-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17844 Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolveambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications.
tech,29-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17850 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.
measure(ment),37-2-C92-1055,bq	Owing to the problem of <term> insufficient training data </term> and <term> approximation error </term> introduced by the <term> language model </term> , traditional <term> statistical approaches </term> , which resolve <term> ambiguities </term> by indirectly and implicitly using <term> maximum likelihood method </term> , fail to achieve high <term> performance </term> in real applications .	#17858 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 using maximum likelihood method, fail to achieve highperformance in real applications.
measure(ment),13-3-C92-1055,bq	The proposed method remedies these problems by adjusting the parameters to maximize the <term> accuracy rate </term> directly .	#17876 The proposed method remedies these problems by adjusting the parameters to maximize theaccuracy rate directly.
lr,12-4-C92-1055,bq	To make the proposed algorithm robust , the possible variations between the <term> training corpus </term> and the real tasks are also taken into consideration by enlarging the <term> separation margin </term> between the correct candidate and its competing members .	#17892 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.
other,26-4-C92-1055,bq	To make the proposed algorithm robust , the possible variations between the <term> training corpus </term> and the real tasks are also taken into consideration by enlarging the <term> separation margin </term> between the correct candidate and its competing members .	#17906 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.
measure(ment),1-6-C92-1055,bq	The <term> accuracy rate </term> of <term> syntactic disambiguation </term> is raised from 46.0 % to 60.62 % by using this novel approach .	#17927 Theaccuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach.
tech,4-6-C92-1055,bq	The <term> accuracy rate </term> of <term> syntactic disambiguation </term> is raised from 46.0 % to 60.62 % by using this novel approach .	#17930 The accuracy rate ofsyntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach.


	in Help