| N13-2009 | evaluating the resulting system on | textual entailment recognition | . To do this , we cast the RTE |
| D11-1061 | redundancy in the framework of | Textual Entailment Recognition | . We also provide quantitative |
| N10-1146 | lexical-syntactic features for | textual entailment recognition | . We have experimentally shown |
| D15-1185 | apply the " soft " logic to the | textual entailment recognition | task . 2.1 Extracting Predicates |
| P09-2015 | for this would be a successful | Textual Entailment Recognition | ( TE ) system . ( Dagan et al. |
| P09-1034 | features we integrate through the | textual entailment recognition | paradigm . 8 Conclusion and Outlook |
| D09-1122 | Web </title> Kentaro Abstract | Textual entailment recognition | plays a fundamental role in tasks |
| P06-1051 | . 2 Related work Although the | textual entailment recognition | problem is not new , most of |
| P06-1051 | evaluated on previous automatic | textual entailment recognition | approaches . 5 Refining cross-pair |
| E12-1036 | use edits as training data for | textual entailment recognition | . In addition to manually labeled |
| D11-1061 | detection ( Dolan et al. , 2004 ) and | textual entailment recognition | ( Dagan et al. , 2006 ) ( RTE |
| D09-1010 | important for applications like | textual entailment recognition | , question answer - ing , and |
| D14-1027 | Moschitti et al. , 2007 ) , and | textual entailment recognition | ( Zanzotto and Moschitti , 2006 |
| D15-1185 | Mahdisoltani et al. , 2014 ) to help the | textual entailment recognition | task . The facts in YAGO have |
| P07-1042 | Parmentier , 2005 ) , to support | textual entailment recognition | or answer detection in question |
| D09-1010 | , and machine translation . In | textual entailment recognition | , it is not hard to see graphs |
| N10-1146 | lexical-syntactic kernels for | textual entailment recognition | . We build on the kernel described |
| P09-1043 | tasks ( machine translation , | textual entailment recognition | , question answering , information |
| J09-3004 | prominent component within the | textual entailment recognition | paradigm , which models semantic |
| J12-1002 | currently used in applications such as | textual entailment recognition | , although the lack of a theory |
