Error analysis of Semantic Filtering Type of error Example sentence Number and percentage of errors FBSF DFBSF CBSF (805)(359)(272 Entity Einstein 's theory of relativity 179 129 105 Recognition explained mercury's motion (2.2%)(359%)(38.6‰) Entity Bill said all this to make the poin 537 182 130 Disambiguation that Christianity is eminently (667%)(50.7%)(47.8%) Subordinate "Bruce S Winters worked at United 89 37 Clause States Technologies research (1.1%)(134%)(136%) Center, bought a Ford Finding #1: Entity disambiguation is the major error factor Entity disambiguation is a tough research problem in nlp community. the type information of relations are not sufficient to further prune out mismatching entities during semantic filtering process Finding #2: CBSF performs the best For example by using context the number of incorrect entities caused by disambiguation can be dramatically reducedError Analysis of Semantic Filtering Type of error Example sentence Number and percentage of errors FBSF (805) DFBSF (359) CBSF (272) Entity Recognition “Einstein ’s theory of relativity explained mercury ’s motion.” 179 (22.2%) 129 (35.9%) 105 (38.6%) Entity Disambiguation “Bill said all this to make the point that Christianity is eminently.” 537 (66.7%) 182 (50.7%) 130 (47.8%) Subordinate Clause “Bruce S. Winters, worked at United States Technologies Research Center, bought a Ford.” 89 (11.1%) 48 (13.4%) 37 (13.6%) Finding #1: Entity disambiguation is the major error factor. Entity disambiguation is a tough research problem in NLP community. The type information of relations are not sufficient to further prune out mismatching entities during semantic filtering process. Finding #2: CBSF performs the best. For example, by using context, the number of incorrect entities caused by disambiguation can be dramatically reduced. 51