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Mar 10, 2023 •171 likes •258 views

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  5. W(-6#/$&'-/0*+.))'()*\/-6;,- * U'-,$':62%(.;*1'"';.$',D* • k*3%""%(*"/,8%0*,%*.--/--*,8/* +'B>()*%+/B/3>7/D4 *%<*?%$0-*'-* ,%*3%"#.$/*,8/*3%(,/5,-*'(*?8'38*,8/D*%336$=* "You shall know a word by the company it keeps!" * (Firth 1957) * • @"1,."5-B#+()*C"$")(.",6* "/,8%0-*3%"#.$/*,8/*3%(,/5,-*,8.,* %336$*.$%6(0*?%$0-*'(*.*;.$)/*,/5,*3%;;/32%(*,%*0/,/$"'(/*8%?* -'"';.$*,?%*?%$0-*.$/=* **** @"1,."5-B#+()*D60#,9%1"1 *EY.$$'-C*ah_eHB** C#70+%DL>D%#**27%/(% %DL'%+>B'%*#(D'5D+%D'(0%D#%L>@'%+/B/3>7%B'>(/(1+ ** ! ! ! ! !(,6'2%(* 9%"#62()*U'-,$':62%(.;*1'"';.$',D* • !*8.&/*.*)%@=** 1. Gather all of the contexts around each term. • r6;'/*:%6)8,*.* !"# = * 2. Create a feature vector from the contextual evidence for • 7.$@*%$0/$/0* !"# *<%$*;6(38=* the term. • >8/* !"# *-//0-*</;;*.;;*%&/$*,8/*s%%$=* 3. Compute the similarity of pairs of terms by computing • Y.$$D*'-*.;;/$)'3*,%* !"# =* the similarity of their feature vectors. • >8/* !"# *?.-(R,*^6',/*$'#/*D/,==** 4. Rank or cluster the most similar terms. • >8/D*#;.(,/0*.*$%-/*:6-8*.(0*.* !"# *,$//*'(*,8/'$*D.$0=* • >8/*$/3'#/*3.;;/0*<%$*://<C* !"#$ C*.(0*36$$D*#.-,/= * ! ! ! !

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