?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=The+Latent+Relation+Mapping+Engine%3A+Algorithm+and+Experiments&rft.creator=Turney%2C+Peter+D.&rft.subject=Language&rft.subject=Computational+Linguistics&rft.subject=Semantics&rft.subject=Machine+Learning&rft.subject=Artificial+Intelligence&rft.description=Many+AI+researchers+and+cognitive+scientists+have+argued+that+analogy+is+the+core+of+cognition.+The+most+influential+work+on+computational+modeling+of+analogy-making+is+Structure+Mapping+Theory+(SMT)+and+its+implementation+in+the+Structure+Mapping+Engine+(SME).+A+limitation+of+SME+is+the+requirement+for+complex+hand-coded+representations.+We+introduce+the+Latent+Relation+Mapping+Engine+(LRME)%2C+which+combines+ideas+from+SME+and+Latent+Relational+Analysis+(LRA)+in+order+to+remove+the+requirement+for+hand-coded+representations.+LRME+builds+analogical+mappings+between+lists+of+words%2C+using+a+large+corpus+of+raw+text+to+automatically+discover+the+semantic+relations+among+the+words.+We+evaluate+LRME+on+a+set+of+twenty+analogical+mapping+problems%2C+ten+based+on+scientific+analogies+and+ten+based+on+common+metaphors.+LRME+achieves+human-level+performance+on+the+twenty+problems.+We+compare+LRME+with+a+variety+of+alternative+approaches+and+find+that+they+are+not+able+to+reach+the+same+level+of+performance.&rft.publisher=AI+Access+Foundation&rft.date=2008-12-22&rft.type=Journal+(Paginated)&rft.type=PeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F6305%2F1%2FNRC-50738.pdf&rft.identifier=++Turney%2C+Peter+D.++(2008)+The+Latent+Relation+Mapping+Engine%3A+Algorithm+and+Experiments.++%5BJournal+(Paginated)%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F6305%2F