?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Corpus-based+Learning+of+Analogies+and+Semantic+Relations&rft.creator=Turney%2C+Peter+D.&rft.creator=Littman%2C+Michael+L.&rft.subject=Language&rft.subject=Computational+Linguistics&rft.subject=Semantics&rft.subject=Machine+Learning&rft.subject=Artificial+Intelligence&rft.description=We+present+an+algorithm+for+learning+from+unlabeled+text%2C+based+on+the+Vector+Space+Model+(VSM)+of+information+retrieval%2C+that+can+solve+verbal+analogy+questions+of+the+kind+found+in+the+SAT+college+entrance+exam.+A+verbal+analogy+has+the+form+A%3AB%3A%3AC%3AD%2C+meaning+%22A+is+to+B+as+C+is+to+D%22%3B+for+example%2C+mason%3Astone%3A%3Acarpenter%3Awood.+SAT+analogy+questions+provide+a+word+pair%2C+A%3AB%2C+and+the+problem+is+to+select+the+most+analogous+word+pair%2C+C%3AD%2C+from+a+set+of+five+choices.+The+VSM+algorithm+correctly+answers+47%25+of+a+collection+of+374+college-level+analogy+questions+(random+guessing+would+yield+20%25+correct%3B+the+average+college-bound+senior+high+school+student+answers+about+57%25+correctly).+We+motivate+this+research+by+applying+it+to+a+difficult+problem+in+natural+language+processing%2C+determining+semantic+relations+in+noun-modifier+pairs.+The+problem+is+to+classify+a+noun-modifier+pair%2C+such+as+%22laser+printer%22%2C+according+to+the+semantic+relation+between+the+noun+(printer)+and+the+modifier+(laser).+We+use+a+supervised+nearest-neighbour+algorithm+that+assigns+a+class+to+a+given+noun-modifier+pair+by+finding+the+most+analogous+noun-modifier+pair+in+the+training+data.+With+30+classes+of+semantic+relations%2C+on+a+collection+of+600+labeled+noun-modifier+pairs%2C+the+learning+algorithm+attains+an+F+value+of+26.5%25+(random+guessing%3A+3.3%25).+With+5+classes+of+semantic+relations%2C+the+F+value+is+43.2%25+(random%3A+20%25).+The+performance+is+state-of-the-art+for+both+verbal+analogies+and+noun-modifier+relations.+&rft.publisher=Springer&rft.date=2005&rft.type=Journal+(Paginated)&rft.type=PeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F4518%2F1%2FNRC-48273.pdf&rft.identifier=++Turney%2C+Peter+D.+and+Littman%2C+Michael+L.++(2005)+Corpus-based+Learning+of+Analogies+and+Semantic+Relations.++%5BJournal+(Paginated)%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F4518%2F