| A00-2027 | , the results of our discourse | feature analysis | further confirm the usefulness |
| D12-1004 | We use several approaches for | feature analysis | to identify statistically significant |
| D08-1081 | 0.86 for ASR . We can expand the | features analysis | by considering the effectiveness |
| D12-1004 | scale screening . The detailed | feature analysis | we performed also pinpoints key |
| D09-1025 | query log features . Feature by | feature analysis | : The feature families analyzed |
| D12-1018 | narrative measure . Last , our | feature analysis | shows that many of our novel |
| D12-1018 | describe the learning model and our | feature analysis | procedure . 4.1 Entailment features |
| D11-1090 | on the whole labeled data . 3.4 | Feature Analysis | We discussed the choice of using |
| D10-1029 | depend on the target application . | Feature Analysis | We perform experiments to evaluate |
| D09-1035 | intentions and perceptions . 7 | Feature Analysis | We first considered the features |
| C02-2023 | content and simply perform surface | feature analysis | , such as a tabulation of syntactical |
| D09-1035 | dual form , but to facilitate | feature analysis | we expand back to the primal |
| D10-1029 | table was previously used for | feature analysis | in Daum ´ e III and Marcu |
| D12-1018 | indicator . 4.2 Learning model and | feature analysis | The total number of features |
| D12-1004 | with the result in Table 6 . The | feature analysis | performed by emotion reveals |
| D11-1147 | strong retrieval systems . 6.1.2 | Feature Analysis | To investigate the effectiveness |
| D09-1118 | and Section 6.3.3 presents some | feature analysis | . 6.3.1 Varying the number of |
| D09-1025 | 0.01 ( at the 0.95 level ) . 4.3 | Feature Analysis | Feature family analysis : Table |
| A00-2027 | our performance and discourse | feature analyses | and provides new evidence for |
| D09-1033 | than the non-literal one ) 6.2 | Feature Analysis | for the Supervised Classifier |
