<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ekaterina Ovchinnikova</style></author><author><style face="normal" font="default" size="100%">Niloofar Montazeri</style></author><author><style face="normal" font="default" size="100%">Theodore Alexandrov</style></author><author><style face="normal" font="default" size="100%">Jerry R. Hobbs</style></author><author><style face="normal" font="default" size="100%">Michael C. McCord</style></author><author><style face="normal" font="default" size="100%">Rutu Mulkar-Mehta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Abductive Reasoning with a Large Knowledge Base for Discourse Processing</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of IWCS 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a discourse processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. We test the proposed procedure and the obtained knowledge base on the Recognizing Textual Entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.</style></abstract></record></records></xml>