Concordance

Query P05-2008 9 (288.1 per million)

tech,0-1-P05-2008,ak	A new tool for <term> linguistic annotation </term> of <term> SCFs </term> in <term> corpus data </term> is also introduced which can considerably alleviate the process of obtaining <term> training and test data </term> for <term> subcategorization acquisition </term> . <term> Sentiment Classification </term> seeks to identify a piece of <term> text </term> according to its author 's general feeling toward their subject , be it positive or negative .	#10392 A new tool for linguistic annotation of SCFs in corpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition.Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative.
other,8-1-P05-2008,ak	<term> Sentiment Classification </term> seeks to identify a piece of <term> text </term> according to its author 's general feeling toward their subject , be it positive or negative .	#10400 Sentiment Classification seeks to identify a piece oftext according to its author's general feeling toward their subject, be it positive or negative.
tech,1-2-P05-2008,ak	Traditional <term> machine learning techniques </term> have been applied to this problem with reasonable success , but they have been shown to work well only when there is a good match between the <term> training and test data </term> with respect to <term> topic </term> .	#10419 Traditionalmachine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic.
other,31-2-P05-2008,ak	Traditional <term> machine learning techniques </term> have been applied to this problem with reasonable success , but they have been shown to work well only when there is a good match between the <term> training and test data </term> with respect to <term> topic </term> .	#10449 Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between thetraining and test data with respect to topic.
other,38-2-P05-2008,ak	Traditional <term> machine learning techniques </term> have been applied to this problem with reasonable success , but they have been shown to work well only when there is a good match between the <term> training and test data </term> with respect to <term> topic </term> .	#10456 Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect totopic.
other,8-3-P05-2008,ak	This paper demonstrates that match with respect to <term> domain </term> and time is also important , and presents preliminary experiments with <term> training data </term> labeled with emoticons , which has the potential of being independent of <term> domain </term> , <term> topic </term> and time .	#10466 This paper demonstrates that match with respect todomain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
other,20-3-P05-2008,ak	This paper demonstrates that match with respect to <term> domain </term> and time is also important , and presents preliminary experiments with <term> training data </term> labeled with emoticons , which has the potential of being independent of <term> domain </term> , <term> topic </term> and time .	#10478 This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments withtraining data labeled with emoticons, which has the potential of being independent of domain, topic and time.
other,34-3-P05-2008,ak	This paper demonstrates that match with respect to <term> domain </term> and time is also important , and presents preliminary experiments with <term> training data </term> labeled with emoticons , which has the potential of being independent of <term> domain </term> , <term> topic </term> and time .	#10492 This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent ofdomain, topic and time.
other,36-3-P05-2008,ak	This paper demonstrates that match with respect to <term> domain </term> and time is also important , and presents preliminary experiments with <term> training data </term> labeled with emoticons , which has the potential of being independent of <term> domain </term> , <term> topic </term> and time .	#10494 This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain,topic and time.


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