| tech,0-2-P04-2010,bq | <term> resolving German pronouns </term> . <term> | Boosting | </term> , the method in question , combines | #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 | modules </term> that mimics the manual <term> | annotation process | </term> . Although the system performs well | #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 | ensemble learning approach </term> to <term> | resolving German pronouns | </term> . <term> Boosting </term> , the method | #7030 This paper presents a novel ensemble learning approach toresolving German pronouns. |
| tech,10-3-P04-2010,bq | this approach is superior to a single <term> | decision-tree classifier | </term> . Furthermore , we present a standalone | #7066 Experiments show that this approach is superior to a singledecision-tree classifier. |
| tech,24-5-P04-2010,bq | open-domain question answering </term> and <term> | text summarisation | </term> . In this paper , we use the <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 | 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 | that resolves <term> pronouns </term> in <term> | unannotated text | </term> by using a fully automatic sequence | #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 | a standalone system that resolves <term> | pronouns | </term> in <term> unannotated text </term> by | #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 | , combines the moderately accurate <term> | hypotheses | </term> of several <term> classifiers </term> | #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 | using a fully automatic sequence of <term> | preprocessing modules | </term> that mimics the manual <term> annotation | #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 | accurate <term> hypotheses </term> of several <term> | classifiers | </term> to form a highly accurate one . Experiments | #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 | results . This paper presents a novel <term> | ensemble learning approach | </term> to <term> resolving German pronouns | #7026 This paper presents a novelensemble learning approach to resolving German pronouns. |
