Concordance

Query P04-2010 12 >
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tech,0-2-P04-2010,bq	This paper presents a novel <term> ensemble learning approach </term> to <term> resolving German pronouns </term> . <term> Boosting </term> , the method in question , combines the moderately accurate <term> hypotheses </term> of several <term> classifiers </term> to form a highly accurate one .	#7034 This paper presents a novel ensemble learning approach to resolving German pronouns.Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one.
tech,26-4-P04-2010,bq	Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> .	#7095 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manualannotation process.
tech,9-1-P04-2010,bq	This paper presents a novel <term> ensemble learning approach </term> to <term> resolving German pronouns </term> .	#7030 This paper presents a novel ensemble learning approach toresolving German pronouns.
tech,10-3-P04-2010,bq	Experiments show that this approach is superior to a single <term> decision-tree classifier </term> .	#7066 Experiments show that this approach is superior to a singledecision-tree classifier.
tech,24-5-P04-2010,bq	Although the system performs well within a limited textual domain , further research is needed to make it effective for <term> open-domain question answering </term> and <term> text summarisation </term> .	#7122 Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering andtext summarisation.
tech,20-5-P04-2010,bq	Although the system performs well within a limited textual domain , further research is needed to make it effective for <term> open-domain question answering </term> and <term> text summarisation </term> .	#7118 Although the system performs well within a limited textual domain, further research is needed to make it effective foropen-domain question answering and text summarisation.
lr,11-4-P04-2010,bq	Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> .	#7080 Furthermore, we present a standalone system that resolves pronouns inunannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process.
other,9-4-P04-2010,bq	Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> .	#7078 Furthermore, we present a standalone system that resolvespronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process.
other,11-2-P04-2010,bq	<term> Boosting </term> , the method in question , combines the moderately accurate <term> hypotheses </term> of several <term> classifiers </term> to form a highly accurate one .	#7045 Boosting, the method in question, combines the moderately accuratehypotheses of several classifiers to form a highly accurate one.
tech,20-4-P04-2010,bq	Furthermore , we present a standalone system that resolves <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence of <term> preprocessing modules </term> that mimics the manual <term> annotation process </term> .	#7089 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence ofpreprocessing modules that mimics the manual annotation process.
tech,14-2-P04-2010,bq	<term> Boosting </term> , the method in question , combines the moderately accurate <term> hypotheses </term> of several <term> classifiers </term> to form a highly accurate one .	#7048 Boosting, the method in question, combines the moderately accurate hypotheses of severalclassifiers to form a highly accurate one.
tech,5-1-P04-2010,bq	This paper presents a novel <term> ensemble learning approach </term> to <term> resolving German pronouns </term> .	#7026 This paper presents a novelensemble learning approach to resolving German pronouns.


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