| other,8-1-P05-1076,ak | describes a novel system for acquiring <term> | adjectival subcategorization frames ( SCFs ) | </term> and associated <term> frequency information | #10275 This paper describes a novel system for acquiringadjectival subcategorization frames ( SCFs ) and associated frequency information from English corpus data. | |
| other,16-1-P05-1076,ak | frames ( SCFs ) </term> and associated <term> | frequency information | </term> from <term> English corpus data </term> | #10283 This paper describes a novel system for acquiring adjectival subcategorization frames (SCFs) and associatedfrequency information from English corpus data. | |
| other,19-1-P05-1076,ak | <term> frequency information </term> from <term> | English corpus data | </term> . The system incorporates a <term> | #10286 This paper describes a novel system for acquiring adjectival subcategorization frames (SCFs) and associated frequency information fromEnglish corpus data. | |
| tech,4-2-P05-1076,ak | </term> . The system incorporates a <term> | decision-tree classifier | </term> for 30 <term> SCF types </term> which | #10294 The system incorporates adecision-tree classifier for 30 SCF types which tests for the presence of grammatical relations (GRs) in the output of a robust statistical parser. | |
| other,8-2-P05-1076,ak | decision-tree classifier </term> for 30 <term> | SCF types | </term> which tests for the presence of <term> | #10298 The system incorporates a decision-tree classifier for 30SCF types which tests for the presence of grammatical relations (GRs) in the output of a robust statistical parser. | |
| other,16-2-P05-1076,ak | </term> which tests for the presence of <term> | grammatical relations ( GRs ) | </term> in the output of a <term> robust statistical | #10306 The system incorporates a decision-tree classifier for 30 SCF types which tests for the presence ofgrammatical relations ( GRs ) in the output of a robust statistical parser. | |
| tech,26-2-P05-1076,ak | relations ( GRs ) </term> in the output of a <term> | robust statistical parser | </term> . It uses a powerful <term> pattern-matching | #10316 The system incorporates a decision-tree classifier for 30 SCF types which tests for the presence of grammatical relations (GRs) in the output of arobust statistical parser. | |
| tech,4-3-P05-1076,ak | parser </term> . It uses a powerful <term> | pattern-matching language | </term> to classify <term> GRs </term> into <term> | #10324 It uses a powerfulpattern-matching language to classify GRs into frames hierarchically in a way that mirrors inheritance-based lexica. | |
| other,8-3-P05-1076,ak | pattern-matching language </term> to classify <term> | GRs | </term> into <term> frames </term> hierarchically | #10328 It uses a powerful pattern-matching language to classifyGRs into frames hierarchically in a way that mirrors inheritance-based lexica. | |
| other,10-3-P05-1076,ak | </term> to classify <term> GRs </term> into <term> | frames | </term> hierarchically in a way that mirrors | #10330 It uses a powerful pattern-matching language to classify GRs intoframes hierarchically in a way that mirrors inheritance-based lexica. | |
| other,17-3-P05-1076,ak | hierarchically in a way that mirrors <term> | inheritance-based lexica | </term> . The experiments show that the system | #10337 It uses a powerful pattern-matching language to classify GRs into frames hierarchically in a way that mirrorsinheritance-based lexica. | |
| other,10-4-P05-1076,ak | that the system is able to detect <term> | SCF types | </term> with 70 % <term> precision </term> and | #10350 The experiments show that the system is able to detectSCF types with 70% precision and 66% recall rate. | |
| measure(ment),15-4-P05-1076,ak | detect <term> SCF types </term> with 70 % <term> | precision | </term> and 66 % <term> recall rate </term> . | #10355 The experiments show that the system is able to detect SCF types with 70%precision and 66% recall rate. | |
| measure(ment),19-4-P05-1076,ak | 70 % <term> precision </term> and 66 % <term> | recall rate | </term> . A new tool for <term> linguistic | #10359 The experiments show that the system is able to detect SCF types with 70% precision and 66%recall rate. | |
| tech,4-5-P05-1076,ak | recall rate </term> . A new tool for <term> | linguistic annotation | </term> of <term> SCFs </term> in <term> corpus | #10366 A new tool forlinguistic annotation of SCFs in corpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition. | |
| other,7-5-P05-1076,ak | <term> linguistic annotation </term> of <term> | SCFs | </term> in <term> corpus data </term> is also | #10369 A new tool for linguistic annotation ofSCFs in corpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition. | |
| other,9-5-P05-1076,ak | annotation </term> of <term> SCFs </term> in <term> | corpus data | </term> is also introduced which can considerably | #10371 A new tool for linguistic annotation of SCFs incorpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition. | |
| other,22-5-P05-1076,ak | alleviate the process of obtaining <term> | training and test data | </term> for <term> subcategorization acquisition | #10384 A new tool for linguistic annotation of SCFs in corpus data is also introduced which can considerably alleviate the process of obtainingtraining and test data for subcategorization acquisition. | |
| other,27-5-P05-1076,ak | <term> training and test data </term> for <term> | subcategorization acquisition | </term> . <term> Sentiment Classification </term> | #10389 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 forsubcategorization acquisition. |
