Recent work on Textual Entailment has shown a crucial role of knowledge to support entail- ment inferences. However, it has also been demonstrated that currently available entail- ment rules are still far from being optimal. We propose a methodology for the automatic ac- quisition of large scale context-rich entailment rules from Wikipedia revisions, taking advan- tage of the syntactic structure of entailment pairs to define the more appropriate linguis- tic constraints for the rule to be successfully applicable. We report on rule acquisition ex- periments on Wikipedia, showing that it en- ables the creation of an innovative (i.e. ac- quired rules are not present in other available resources) and good quality rule repository.
Extracting Context-Rich Entailment Rules from Wikipedia Revision History
Magnini, Bernardo;
2012-01-01
Abstract
Recent work on Textual Entailment has shown a crucial role of knowledge to support entail- ment inferences. However, it has also been demonstrated that currently available entail- ment rules are still far from being optimal. We propose a methodology for the automatic ac- quisition of large scale context-rich entailment rules from Wikipedia revisions, taking advan- tage of the syntactic structure of entailment pairs to define the more appropriate linguis- tic constraints for the rule to be successfully applicable. We report on rule acquisition ex- periments on Wikipedia, showing that it en- ables the creation of an innovative (i.e. ac- quired rules are not present in other available resources) and good quality rule repository.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
