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154 12 ( ) Vol.54 No.2 2015 3 JournalofXiamenUniversity (NaturalScience) Mar.2015 doi:10.6043/j.issn.0438 0479.2015.02.

1 ( ) Vol.54 No JournalofXiamenUniversity (NaturalScience) Mar.2015 doi: /j.issn R, ( ]a G,9O ) 56: % 5 ) B` M 7 # <, [ <# 67 < # 9 Z V _ D H,W H < 6 7 # # #4 #(7 R, Z X D H 5 4 # ( 7. D H 0 1 b, (IDF)(767,WH T#X D G T # Z " J, Z 89 TZ "# T(767 : (inverseconceptfrequency,icf). - D H # C Q, 6718 T 1 T T G T#ICF(7,X4 # TICF(7 H#ICF(7. < ICF 9 #XD,_ 67 - # ` J LZICF7 > = 6 7 #. J & ] 7 [ % = 72.28%, 15.87% = 49.64%. 789: ; ; ; T(7 :; : TP391.1 # <: A #=' : (2015) # ] #. c, 5# ] Z # ] H ] < #!. ] +, <&]# H b # ] M ] PQ, V# 67 V # H< # 4@ # ]M, = #67F Y H7. R #C, V # H # S- = # 67? #, 01T # b. % 5 ) B ` 9 M 7 # <. 01 \# <[ DH?,89 V#67, '"CQ V #$ G 67 # 8 9 ] 6 #, < 4,0 1 $, G V Z # 9, Z 67 # ]6# 6 7 : R 8, #, $ R. 4 ]6 Z J # $,C! U, < #$ ]# D H,_ 6 7 # \, [1] < % 6 7 V# 6 7 : R 8,? 8 # %,G G '" D 8 V C V #M [2], 6 7, H 0 1 # b,g [ V # 6 7 V #, 0 1 # V # [3 4] V# 967 _ J. [ 6 7 <, < # 8 9 # R, M#G Y ', [5]0 1 >?@ : AB@ : CD: _B`a ( );9O B`a (2013J05099) E :zdz@xmu.edu.cn # :, # 67[J]. :B`a,2015,54(2): 犆犻狋犪狋犻狅狀 :SunJing,ZhangDongzhan.Wordsimilaritycomputingbasedoninverseconceptfrequencies[J].JournalofXiamenUni versity:naturalscience,2015,54(2): (inChinese) 犺狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

2 258 (B`a ) 2015 < T/ #D,67 T#! 9 # [6]^ _ T # 6 7, [7] Z ] 2 # 6 7, [8] Z C #C T%, [9] Z # ( 7 D U. R 0 1, X T T T #XD, T # 6 7 D ( 7. < H T (7, # (7 6 7 #. ` R F,R 7 J * T(7# R W" [10] ( HowNet) 3 W A _X N O#8 # ], C G V # # % G % V #, '" ' #? # T, < T R9 R C #, T D #. 8 <8 C,8 <8C T C, # 67 # T# 67. < ]DH #/. 1 狑 H8, 犠 H #!,Z 狑犠. 2 Sim( 狑 1, 狑 2).? 狑 1 狑 2, H8 0 1V #,?@ H1,( ( H0 [5]. 3 Dis( 狑 1, 狑 2).? 狑 1 狑 2, '" CQ# HH b,? #. '"V #8 $ #, R #^, HH. V J [5],? 狑 1 狑 2,Dis( 狑 1, 狑 2 ), Sim( 狑 1, 狑 2), V 4 犮 H #8, 犆 H #!,Z 犮犆. 5 犅 ( 犮犻 )= 狑, 犮犻犆 H 犮犻 < R 狑. 6 犉 ( 狑 )H 狑 #!. 8 狑, R 8, H 狑 # 75R,R H 犉 ( 狑 )={ 犮 1, 犮 2,, 犮犻,, 犮狀犮犻犆, 犅 ( 犮犻 )= 狑,1 犻狀 }, 犉 ( 狑 ) 犆 ? 狑 1 狑 2, [5] Z 狑 1 狑 2 # HG #,Z Sim( 狑 1, 狑 2)= max 犮犻犉 ( 狑 1 ), 犮犼犉 ( 狑 )Sim ( 犮犻, 犮犼 ). (1) 2 6 H 67, T V C #, 67 T# T Y Z < C 1 # D#, T $ 8. T'"V, -67 T. # 67 T, [5]? T R '"V # H 犱, R? TV # : Sim( 狆 1, 狆 2)= α 犱 +α, ( 2), 狆 1 狆 2 R? T, 犱狆 1 狆 2 T'" V # ^,α α # G H0.5 # 狔 H #8 T, 犢 H T #!,Z 狔犢.,8 犮犻 <8C TC C!, TC TG # T C :< ( V ) _ C; TC :< T= T= ( V ( T = V ) C; T C :< ( V ) C; 8 犘 ( 犮犻 )={ 狔 1, 狔 2,, 狔犻,, 狔狀狔犻犢,1 犻狀 }H 犮犻 # T!. 9 犘 1( 犮犻 )H 犮犻 #18 TC # T!,18 TC Z 犮犻 Z #18 TC. 0 犘 1( 犮犻 ) 1, 犘 1( 犮犻 ) 犘 ( 犮犻 ). 10 犘 2( 犮犻 )H 犮犻 # 1 T C # T!, 18 TC # TC H 犮犻 # 1 TC, 犺狋狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

3 12 : # 犘 2( 犮犻 ) 犘 ( 犮犻 ). 11 犘 3( 犮犻 )H 犮犻 # T C # T!, 犘 3( 犮犻 ) 犘 ( 犮犻 ). 12 犘 4( 犮犻 )H 犮犻 # T C # T!, 犘 4( 犮犻 ) 犘 ( 犮犻 ). 犘 ( 犮犻 )= 犘 1( 犮犻 ) 犘 2( 犮犻 ) 犘 3( 犮犻 ) 犘 4( 犮犻 ) [5] Z #, < 4 / 犮犻, 犮犼 # : 1)18 T C,R 8 6 D # Sim 1 ( 狔 1, 狔 2)( 狔 1 犘 1( 犮犻 ), 狔 2 犘 1( 犮犼 )),6 7 (2); 2) 1 TC,R86D# H Sim 2 ( 狔 1, 狔 2),67 $: (i) Z 犘 2( 犮犻 ) 犘 2( 犮犼 ) # # O,D 67, #8C, I$ 犘 2( 犮犻 ) 犘 2( 犮犼 ) K RC#2 (i)7=? (i), 犘 2( 犮犻 ) 犘 2( 犮犼 )H. (i)67(i) # #7 3) T C,R 8 6 D # H Sim 3 ( 狔 1, 狔 2),67 X T # C DH 8 C, 6 7, Q C # 7 4) C,R 8 6 D # H Sim 4 ( 狔 1, 狔 2),67 X # D H 8 C, #?!, 2) 67 67R2!#, Q G / # #7 < H 6 D # " 6 D # <, 6D J,1W" 6D# V # <? RS # V H: 4 犻 Sim( 犮 1, 犮 2)= β 犻 Sim 犼 ( 狔 1, 狔 2), 犻 =1 犼 =1, β 犻 (1 犻 4), β 1+β 2+ β 3+ β 4 =1, β 1 β 2 β 3 β 4.Q@ Sim1 Sim 4 V # < "'. 2 + ( 犻狀狏犲狉狊犲犮狅狀犮犲狆狋犳狉犲狇狌犲狀犮狔, 犐犆犉 ) 9 13 TXD. 犐 ( 犮犻, 狔犼 )( 狔犼犘 ( 犮犻 )) H T 狔犼犮犻 #XD,Z 犮犻 X D 1 #, 狔犼 T 犘 ( 犮犻 ) # (7. 14 / XD. 犐 ( 犮犻, 犘犼 ( 犮犻 ))(1 犼 4)H/ 犼 ( 犼 H H,1 1 8 T C,2 1 TC,3 TC,4 C ) 犮犻 #XD. 犐 ( 犮犻, 犘犼 ( 犮犻 ))=max{ 犐 ( 犮犻, 狔犽 )( 狔犽犘犼 ( 犮犻 ))}. XD # Z, < 犘犼 ( 犮犻 )(1 犼 4) # 4 / 6 7 2, Sim 犼 ( 狔 1, 狔 2)(1 犼 4) #(7 β 犼 G 犐 ( 犮犻, 犘犼 ( 犮犻 )) 67#,Z β 犼 - 犐 ( 犮犻, 犘犼 ( 犮犻 )) # R >. # 犮犻, 犐 ( 犮犻, 犘犼 ( 犮犻 ))(1 犼 4)# #. &]# T( # <* # T(7,R `] β 犼 - 犐 ( 犮犻, 犘犼 ( 犮犻 )) # > #.R9> # 犐犆犉 TF IDF ( [11], Z < ICF67 T # ( 7,ICF ( ]6, < TZ # ( R TXD #, T#7 %_ Z # HJ$. 15 ICF 8 T X D # % # 5. 8 T 狆犻 #ICF, # R T 狆犻 Z 犘犼 ( 犮犻 )( 犮犻犆,1 犼 4) #, X # ICF( 狆犻, 狘犆狘犼 )=log 狘犘犼 ( 犮犻 ) (1 犼 4, 狆犻狘犘犼 ( 犮犻 ), 犮犻犆 ), (3) 狆犻 H # T, 犼 H T ], 犆 H!. 16 ICF_ 犆 ( 犮犻, 狔犼 )( 狔犼犘 ( 犮犻 ))H T 狔犼犮犻 #ICF(7. 8 犮犻, T#ICF ( 7 R T #ICF (7, ICF_ 犆 ( 犮犻, 狔犼 )=ICF( 狔犼, 犽 )( 犽, 狔犼犘犽 ( 犮犻 )). T # 7 % _ Z # HJ$, Z, 犆 #, ICF_ 犆 ( 犮犻, 狔犼 )# G 犐 ( 犮犻, 狔犼 )#J H λ.z lim ICF_ 犆 ( 犮犻, 狔犼 )=λ 犐 ( 犮犻, 狔犼 )( 犮犻犆,λH8 狘犆狘 ). 犺狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

4 260 (B`a ) ICF_ 犆 ( 犮犻, 犘犼 ( 犮犻 ))=max{icf_ 犆 ( 犮犻, 狔犽 )( 狔犽犘犼 ( 犮犻 ))}HG / 犮犻 #ICF( [5] < * ( 6 7, <ICF67/ ( _ 67 V,A D 6 D " 6 D # <,4 / # V# # < ICF, ', < $ (4)67? RS # V : 4 Sim( 犮犼, 犮犽 )= β 犻 Sim 犻 ( 狔犼, 狔犽 ), (4) 犻 =1, β 犻 (1 犻 4) ICF 6 7 Z # ( 7, β 1+β 2+ β 3+ β 4= 犐犆犉 9 犮犆, 犮犻犆 ( 犻 =1,2,, 犆 ), < (4)67 犮犮犻 # Word_Simi( 犮, 犮犻 ). 犮狀 (0< 狀 4) / 狆 11, 狆 12,, 狆 1 狀, 犮犻犿 (0< 犿 4) / 狆 21, 狆 22,, 狆 2 犿. : ICF# 677 c : 犠 c : G 犠 = # : 1) T. # 8 T 狔犻,D 6 7 T 狔犻 Z 18 T 1 T T #". 2) # 8 犮犻 ( 犻 1), 犮犻 # 8 / 犘犼, 犘犼 #ICF #8, ICF. 犘犼 3) # 8 犮犻,67 犮犻犮 # Word_Simi( 犮, 犮犻 )= 1 ICF 狆 Sim( 1 犻狆 1 犻, 狆 2 犻 ), 犻 4 ( 狆 1 犻犘犻 ( 犮 ), 狆 2 犻犘犻 ( 犮犻 )). 4) 1) 13)[ # 6 7 犠 # 5) = E, E af7 > %, Z 8 9,! 犆 # 犮犻 # 犐 ( 犮犻, 犘犼 ( 犮犻 ))(1 犼 4) #, 8 α(0<α 1)? # 犮犻犮犼, 犐 ( 犮犻, 犘犽 ( 犮犻 ))- 犐 ( 犮犼, 犘犽 ( 犮犼 )) α( 1 犽 4).R! # #. X 犆 # O$ ICF # 6 7 J * ( 7 7 ^ _ # 67. #4 #(7 β 犼 (1 犼 4),Z β 犼 - 犐 ( 犮犻, 犘犼 ( 犮犻 )) # R >. X β 犼 * H 4 α 犼 (1 犼 4),, 犮犻 α 犼 - 犐 ( 犮犻, 犘犼 ( 犮犻 )) α/2. ICF 7, ( 7 ICF_ 犆 ( 犮犻, 犘犼 ( 犮犻 )) lim (ICF_ 犆 ( 犮犻, 犘犼 ( 犮犻 ))=λ 犐 ( 犮犻, 犘犼 ( 犮犻 ))( 犮犻犆,λH 狘犆狘 8 )), 1 犐 ( 犮犻, 犘犼 ( 犮犻 ) = 1, 犼 4 ICF_ 犆 ( 犮犻, 犘犼 ( 犮犻 ))(1 犼 4)##8 H1/λ., ICF# 67, Z#4 #(7 β 犼 (1 犼 4) lim β 犼 = 犐 ( 犮犻, 犘犼 ( 犮犻 ))( 犮犻犆 ). 狘犆狘 F 8 H 犖,G 犆犖 β 犼 - 犐 ( 犮犻, 犘犼 ( 犮犻 )) <α/2,r F 犆 #[ $, ICF# 67J* (77 ^_ # 犆 =,, 犆 # 9 Hafairs ),military,; 犆犻 =, 犆犻 # 9 Hinstitution,military. 2.3 #67, -#8 Q 犆 #ICF=[ ,0.7,0.01,0.05], 犆犆犻 # T 1 T T CH[0.2316,1,1, 1], Sim( 犆, 犆犻 )= < [5]# 7,! [6] T 6 7 # ^ _, Z Sim ( 犆, 犆犻 )= , LZ, X \ 67#F W, 8 #, V H8 8 # $ V#, #O Z 8 # [10]. # % E_C %,R % 9 H` %.` %< HD #R, #? H `,8 buy!, 8 weave (. W_C=,, G_C=V, E_C=~,~ Z,~[, ~, ~, DEF=buy!, W_C=,, E_C=~ \,~ ],~ S ^,~ b _,~ 8 ) `, ~ab,~, G_E=V, DEF=weave (. # c,#1 $ 犺狋狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

5 12 : # $ G # #67 \ # #67#J, X 8 H # [9]? # # H? #.G [10] J, O T$ #, WH [10] #D # D <$ #, 8 # $ # WH < [10]# D YH!. X( E_C %, # # E_C % X ( 8 # _ `DH. 3 FGPT. S 3 1 FGPT < VisualStudio2008 Window 7 ] ICF # T ( 7 6 7, < (1)6 7, T T'". # [5] 67 T ` 6D R%#, <6 D 45 ^ V5 H `, # _ 6 7, = E. GD # 5 ", < # # D ( ]8#, < R!# C `. 1, D <5~6! H % ]6!,,! ( 狓, 狔 )R ` #, [ 狓 # `X 7 ( 1) [6]7 ( 2) [5]7 ( 3)397 J, 1 L Z 7 6D # c>. 3 2 FGPT S ICF ( R # 8 : XD # T R 1 1 # T Z "# T, XD #.R 9! # W #. <ICF( T(7#, & ] ` T* (7, 67 (7G 4XD >. ` L Z, <ICF ( T (7# R9> # ICF T 4XD #89, 89 4 # (, WH # T 7, # T ( <,Y`R ( H #.J # T > T, 18 T Z " 364, > 1 T Z " 263, 67 T(7 Ya7 >,R ] _ # c, > # =#,] 6 ` [6] # 7 R H, EU 18 T 1 T #X, # L,R 6 7 # # ] # HS, ` L Z, E [ # T > T #. 4 P " _ # c, D < C 1 Fig.1 Accuracyrateandrecalrate 犺狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

6 262 (B`a ) 2015, T T R # ]5, T#ICF T ( 7. # 6 7 `, < X #4 T(7D G4 T, G #, Q < [5] (1) = # ` af,gd # V #>. D # c> ^,T, ICF67 T ( # % -,_ # _ [ Y #, # T9 9 # D. Q#01,7! = ICF 67 T(77 # %, 7 < Y #, R 01 < D # : [1] B` % ( ] D 3O[D]. _: a ,2001. [2] Rodríguez M A,Egenhofer M J.Determiningsemantic similarityamongentityclassesfrom diferentontologies [J].Knowledgeand Data Engineering,IEEE Transac tions,2003,15(2): [3] BudanitskyA,HirstG.Semanticdistancein WordNet:an experimental,application orientedevaluationoffivemeas ures[c] Workshopon WordNetandOtherLexicalRe sources.pennsylvania:thepennsylvaniastate Universi ty,2001:1 6. [4] LeeW N,ShahN,SundlassK,etal.Comparisonofontol ogy basedsemantic similaritymeasures[c] AMIA An nualsymposiumproceedings.bethesda MD,USA:Amer ican MedicalInformaticsAssociation,2008: [5], O. # 67[J]. 67,2002,7(2): [6] 9S,$,,.89^_# # 67[J]. ],2008,22(5): [7] 7,0 # 7 01[J]. ],2010,24(6): [8],^_# # 7 [J].6 7 <,2011,31(11): [9], b.^_# # 6 7[J].67 <,2013,8: ,2288. [10] X,X. [DB/OL].[ ].htp: [11],,,.8 9 / 7 # D / ( [J] G. c,2009,10: 犠狅狉犱犛犻犿犻犾犪狉犻狋狔犆狅犿狆狌狋犻狀犵犅犪狊犲犱狅狀犐狀狏犲狉狊犲犆狅狀犮犲狆狋犉狉犲狇狌犲狀犮犻犲狊 SUNJing,ZHANGDong zhan (SchoolofInformationScienceandEngineering,XiamenUniversity,Xiamen361005,China) 犃犫狊狋狉犪犮狋 : Thewordsimilaritycomputationplaysanimportantroleinserviceselection,naturallanguageprocessing,andliterature retrieval.currentresearchesofwordsimilarityaregeneralybasedonhownet.byanalyzingthestructureofhownet,wepresentan ideathattheweightoffourbasicstructuresoftheconceptshouldbedynamicalygeneratedduringcomputingthesimilaritybetween twowordsandamethodofcalculatingtheweightofprimitivebasedonthefrequency.wecomputetheicfofeachbasicprimitivein thefirstbasicprimitive,otherbasicprimitives,relationprimitiveandmarkprimitivethroughconceptstructureanalyzing,andtakethe maximumicfastheicfofthebasicstructure.thenwecomputethewordsimilaritybyusingdynamicicfobtainedastheweightof fourbasicstructures.experimentalresultsshowthattheaccuracyofwordsimilaritycalculationisefectivelyimproved.theaverage accuracyofformer160wordsrisesfrom30.74% to72.28%,andtherecalrisesfrom15.87% to49.64%. 犓犲狔狑狅狉犱狊 : HowNet ;wordsimilarity;inverseconceptfrequency;primitiveweight 犺狋狋狆 : 犼狓犿狌. 狓犿狌. 犲犱狌. 犮狀

;54 ;1 ( ' ) Vol.54 No JournalofXiamenUniversity (NaturalScience) Jan.2015 doi: /j.issn RSTU V ; &! Y,,, [,2\

$;54 ;1 ( ' ) Vol.54 No JournalofXiamenUniversity (NaturalScience) Jan.2015 doi: /j.issn RSTU V ; &! Y,,, [,2\$ ;54 ;1 ( ' ) Vol.54 No.1 2015 1 JournalofXiamenUniversity (NaturalScience) Jan.2015 doi:10.6043/j.issn.0438 0479.2015.01.011 RSTU V ; &! Y,,, Z,@ [,2\] ( 8+ =,>^ E G,Y7,>^ 361102) ; : ( 犕狔犮犪犾犲狆犺狔犾狅狆犺犻犾犪