DFJMTXNY[RHGQSTVKU]^OPI\`_WLRZE 3=aeg ih>jkclmlm< nmdfb?
yosqrtpr´£ ¯uÂzÎÔ{Ïµ~ÁÕÆ¬¿¶µÞw³·µÐ±³¿|äÁÝ¢¿»µ°¾uÒÒµë×©µÍµÛ±áº¯Þw³ê¥Ç ©À¯ ¿vÖ°¶uìxðí¤½Å ª­Âµ!ØÉ³ñ¾§éÁÐ´}Ë´"«­¹æ§Á³¡ÂÏ~Ìµå$Á³¡v¨À´¼¾uàuÝ¢½À­Â´Æ°®­~Ì¥u}Ë±¡çÙé´¼¾Ó´è#ª¡¿¨±´³¾uÒÍ³²µ"ß´£ ¯»¼¾Ê³|ä±ª¯âß°¦¿¨±¥Ü©À¿vå$´£ ¯¶uÒyãÒ±ª¯Ý¢½À­ÑÚ³ïxî´¬¿É³å$±uwµ~ÌÄ¥ÏµÕÃ¬¿~±²µÈÌ~°¿¤µå$Á¸±º¿v
üöÿú0HNf§­¿ÙÂªqÐ§¬ï28ª ¨¦¿-¤¬Gí¬{½ÝÚ¹¨§¹À*«¿+Ë¿S8ª©¬ÙÂª6É{¬ámf§­¿ñ'¬GÄÌr"Å¨¬{½U¹¨§¿¶¸9)ªH:½)·º¹¨ò@º¹¿b¿º²§¬{«î¹J9§­¿<öNNö¬6É
¬ám½;B¨¦¿»»ª{¬ ½Ú6Éám¬!$6&º¦ [3, 1, 2]À

 G9!$Ä'5§­{ôe¨K°Å½6&Á
7¹ÀÈT¬+Ø­¿_g«È!$½-ÏÄ2 ,Å¿ç¹¤¬{ôe½(± M¿öN?¨öN?¬XÃVTWY½Ú ¿wí¬3ù«)·ª7D¬H:½Ä3 ,ÅÀ«sD½ï
« wí¬{3ù¨{ôe«¶¹ ¨¬çD¬H:½Ä4 ,ÅÀ_M«¿¬-¤¬
DH:«?¦U6½VªÄ5 ,Å¿²¨´¹Ä6 ,ÅÀ@ilùp1xô|4úum A4õøv{oýû@ilp1xôûM«:¹H:Ä3 ,¿ 4 ,Å§µº¹¶«¿º²§«6&º¦¹{ôe¬þ¬QUÃcS
¬S8«ï¥!$¨PÃ]S·ÿ' XÃV½Ý
Ú¹!$§­¿AôÑXÃV«¶¹7Ä3 ,Å¨w
í¬{3ù¨¬ ç7Ä4 ,Å§/1/ø¬{
½Ä5 ,½8nÅÀº­¿QUÃcSS8«õ0¹'92ªû¬kª¿¶¯;õXÃV(ü¬B¡«)¸¿÷Ní¬+Ë8ªîD½ ¸')¦ª¨¿¿'-¤¬HNGÄâ1Å§£¦µ¿ ¬ªF0§¹Àº«?¦¿ Resnik[4] ­¿QUÃº©¬¶ªÌ«¦3ùº¹«¶£¦¿ cSS8«r" ½(/¹¨§'92ªû¬y¬º·¬{µ-ª£¦¹¿º²§¬¶ª 78½x£¦¹ÀÌ):{­%¼º¦ª£ ÀÞÂ¬J9§­¿¬/#¿ÈJ9§­¿PÃ]S·ÿ' ;õXÃV¶ªÌr"½ ¨K¯¿ r"«¶£¦¬ ½ïÈXÃV¨¦Ú¿«+Ë8ªzÔ½8¹
Y6¹{¬ ½6&¿7½Vª£¦ ¨7¬q½`¹À·«¿¬+Ë8ªzÔ¬þ¹À« r"¨]caÃV½(/¦¿Ìr"«¶ÈT§­¿÷NÄâ1Åí¬{35«¦¿PÃ ¹ ¬6½U À]S·ÿ' +Ë8ªD¸#r"½Ú¹¨§¿ r"«4¦â1Ä÷NÅí¬ 6¹ovnýûum|ú02zþôÈ!$§­¿ (1) «;õr"«ï¥{ôe½ïÈ¨¹Àº­¿ ½8´¹÷N?¬4#¨;õ)¹÷N¬;õæ¬½ ¨¦Ú¹µ¬§¸¿ ÷N wp ¬5§¬÷N? (w ; w ) ¬
2

2.1

1

2.2

3 Evaluation of Point-of-View-based Word Similarity Mea-sure employing Semantic Dependency Informationy Kenji Nagamatsu

Hidehiko Tanakafnaga, tanakag@mtl.t.u-tokyo.ac.jp

Faculty of Engineering, University of Tokyo12y4jòøB
3.1
B÷2v{Sim(w ; w ; wp )XPr (wjw ; wp ) + Pr(w jw ; wp )DÄÈ,¿¶¯,¬sDÅ§7 =2{ôe­'5¬µ¬§¹À 13¬oa­ [5] ½8nÀ
w Co w Co w
(1) 1. /÷N?«?¿QUÃcSÄEDR â1>FiÅþ¬y¬;3p(â1¬v¬+¿_µA
§×·º¹À§¿ Co(w) ­÷N w ¬;õ÷N¬û½ ¨¹µ¬ÄdepthÅ
$Y½¿ P r(wjw ; wp) ­÷N w ¨;õ¹÷N w
2. /÷N?«?¹QUÃcSÄEDR â1>F¬;õæ«?¿« (2) «ï¥ ÷NiÅþ¬\ÃZ' ½H°_jdhNyÄ¬7yÅ
wp «¶¹6½8 µ¬§¹À
½
 ¨¹µ¬Älink#Å-¤¬÷Ní¬;õæ­¿º·¬î{¬=½¨U·º¹¿¬î{¬ Y­¿ «¶£ 3. Ê.u 94 0 ·ÿ' ÷N¨¬;õ÷Nr"½ÝÚ¿/÷N?«?¹;3;õ÷N¬¦¼¹µ¬§¹À§¿ P r(wjw ; wp) «?¦¿}õæYGû½
 ¨¹µ¬ÄcoÅ«¶¹&³½(/¹À(1) «¦ r"½UªqY«/ýPr(wjw ; wp )4. Ê.u 94 0 ·ÿ' ¿÷N¨¬}õæ wp ;w
û½QUÃcSÄEDR â1>FiÅ¬ã\ÃPw ) f (x; w )= (  wp;w 0 1)f (w; w ) f+(w;x Co wZ«×¿/÷N?«?¹;3p(â1¬r(2)" _Aû½ ¨¹µ¬Äresnik95Å [4]º·«?¦¿/,§(± !$ pov §]caÃ§¿ f (w; w ) ­÷N w ¨ w ¬;õ ½¿V
½ 7" pov(1.2)¿ pov(1.5)¿ pov(2.0)
(wp ; w) ­ ÷N wp ¨÷N w ¨¬í¬+Ë8ªD¸#îD¬=½û§¹À¬¬+Ë­¿; ½8¦7D½Vª£ À co ¨ pov §ÝÚ¦
¹;õXÃV­¿Ê.u 94 0 ·ÿ' µ¬§õ¹÷N w  ÷N wp ¨î=7¿¹À
Sim(w ; w ; wp ) «?¹ð×µAª¹¨¨«ª¹À
3.2 { 4jòøB
(wp ; w) ¬û¨¦­¿ÈJ9§­+Ë8ªD¸#ºº¬ã{ôe«¦¿¨3ùº¹
r"½VOPÃ]S·ÿ'¿§¬6Lr" (MIC) ½8´¦ÝÚ¹À ¡¿2*«6Lr" SfWQbeZ½¦£ qY«¿×·º ö½8´¹¨§ª ´¿«B½Vª N?Ä10,297 ?Å¨öN?Ä100,000 ?Å¬+¿©
¬·¬éYö¨3ùº¹ÄÅ½7
¿  = mic(w; w ) ¨¦¹À
 H:½x 1Áx 2«ÀöN?¨¦­ IPAL i¬öN^LÃeZ·MIC (w; w )ÿ'÷N?½ Ú¿öN?¨¦­¿ºP r(w; w )= log P r(w)P r(w )º¬{ôe§ Ú¦¹÷Ni+·¿Íd+«.¾¡÷N?½ÄB8«Å Ú¦¹À
 mic(w; w )Pk co(w; w ; rk)² ¿¬D§­¿ pov «×¹ r"¨¦¿= log P co(w; w ; r ) P co(w ; w ; r ) (3) /¨ª¹÷N?þ¬÷N½
Ú Àº­JÂ«
i ji ji;ji;jr"½+5¹¨7],«ª¹ ´§¿¬D§+Ë8ªD¸#r"¨­¿+Ë^fÃ`+§¬ ¬¶ª1>8ªt½0±¹qY«­=%¡¨ ¼
â1¨%9â1í¬å9{½ß D¸#îD½ º¹À¬Oc^­¿¹SfWQbeZ¬5§¿öN?
ÀC>8«­ EDR PÃ]S«¹ main SgWY+¬â1¨¿º«D¹;¬åSgWY+¬â1¿¶ $Y¬¹/5éY½ö¨3ù§¹«¿ö¨Äí¯¬å9{¬7¤3¬ §º¹ÀGc¿ EDR .£¦Å3ùº¦²öN?¬éY½µ¬PÃ]S· 1,254,851 3¬ XÃV co(wm; wr ; r) §¸¿XÃVFOc^þ§5#«(ü¹7¿öN?¨öN?¬¬ Y¨3ùº¹À
½ ¸'¦ÝÚ¦¹À

 Sim(w ; w ; wp) ­¿1

1322

18( 1 )\

22( 2)

0000(())

8 20

100(0200000000)0coverage of non-synonym pairs
100.00%10.00%

coverage of non-synonym pairs
100.00%90.00%
depth
80.00%
link#
70.00%
copov(1.2)
60.00%
pov(1.5)
pov(2.0)
50.00%
resnik95

depth
link#
copov(1.2)
pov(1.5)
pov(2.0)
resnik95

40.00%
30.00%

1.00%

0.10%
20%

20.00%

30%

40%

50%
60%
70%
coverage of synonym pairs

80%

90%

100%

10.00%
80%

82%

84%

86%

88%
90%
92%
94%
coverage of synonym pairs

96%

98%

x 1: öN?«?¹öN?

x 2:  (x 1¬äA)~ûvnkú÷2}w/,¬H:·­JÂ¬÷N?«?¹ û=%©¿ª¼¢wí¬3ù¨¹7 ¬6î½
¤û©½3ù§ªÀ§¿sD½3
¦wí3ù¹ ¨¬ ç½Vª£ À÷N?¬UÃS IPAL N
ÕOª÷N?y 2924) ¬Aû 3.204
2.511
 1: E¨ª£ i¨¬÷N?¬{4100%w
D­¿/,§(± {ôeºº«¶¸8´!$ ÷N?1> IPAL ÷N? N ¨¿wí¬3ù«¶¸×·º  ¨¬
depth
0.380
0.164
0.449
ç§VªÀD­QèFAè1}Äú{Å 14 Î§link#
0.365
0.1040.442
¸¿·´×·º ÷N? 100 J«?¦¿ 1co0.3440.211
0.306
Ä{ªÅ· 5 Äê1ª'öÅ²§¬) ½×0.390
0.210
0.415
pov(1.2)
¹À
pov(2.0)
0.424
0.232
0.495
¬D§Ú ÷N?­¿ IPAL i¬öN?
resnik95
0.426
0.235
0.420
·Íd+« 50 J¿² N¬_5(WÒ+· 2: ã!$«¶¹ ¨wí¬) ¨¬6îDy
Íd,«.¾¡÷N?· 50 J¿G 100 J¬÷N?½
Ú¦¹À4#¨µ÷N?«­öNîD¹¨K!)¹¿QUÃcS§¹N¬÷N?­¿IPAL öN?¶¸µö{¨Ö5º¹Ä º¬÷N?§¬) ¬Aû½ 1«À«¿ã!$«¶¸G9  ¨wí3ù1 8nÅÀ) ¨¬6îDy½ 2«À§­¿÷N?1>§8´ 6îDy¨;«¿ãi¨¬÷N?+§¬64.2wqCîDyµ¦¦¹ÀD« Ú ÷N? 100 J¬+¿ 3 ,¬ã!$±¦§b« û8²£ µ¬­ 62 J§£ À 5 s¿ãD× ) ­ãw¬ì«ï¥
x 1Áx 2·¿÷N¬;õr"½Ú {ôeµ¬§¸¿÷N?«¶£¦­Aª®·¤½
¨¹ ´¿'5§­R«) ¬e^ 1.0 '5 ­¿3592ª÷NÄâ1ÅÝÚ PÃ]S«):¹¨ÓB¹¨­H¿QUÃcSS8½Ú¬÷N? 53 J«I£¦I&½8¹¨«¹À
{ôe¶¸µ~ ­X¨H¹À¦¿²¿÷N?½ÿ' i¬.«¶¹{¬Y¬.½0±¹ ´«¿ IPAL i¿Nº co «?¦+Ë8D¸#r"¨ r"«¶¹&³
4.1

link#
depth
link#
co
p(1.2)
p(2.0)

co0.970 0.132
0.198

p(1.2)

0.175
0.247
0.938

p(2.0)

0.211
0.268
0.809
0.942

res95

0.910
0.883
0.125
0.200
0.249

 3: ã!$í§¬6îDy½Vª£ pov ­¿MdR[e¶¸µX~½¨º Àx 1§­¿]caÃV ¬.¯#«¶¸Ä = 1:5 or
2:0Å¿ co ¶¸µ~ Ü¢¹qY¹¿¨¦­¼§¸¿² {ôeb«ÝÚº¹qY§­¿öN¬ 80% ½ª©¬+5ªº¹ ´¿8ªÓB­ªÀ«¿ 2¬H:¶¸¿÷N?1>§K qY¿QUÃcS«ï¥{ôeÄdepth, link#, resnik95Å
­;õr"¬³«ï¥ co ¶¸µ¿wí¬3ù¨¬X6î½¨¹Àº­¿QUÃcS>
wí¬3ù½ï«S|º µ¬§¸¿¬2?2*«Gº¦¹·¡¨U·º¹À¿;õr"« 2 ,§(± &³½Vª¨§Äpov(1.2), pov(2.0)Å¿wí¬3ù¨¬6î­º¶¸µXª¸¿9)ª]caÃVÄpov(2.0)Å½.C¹¨§¿7 !$¬þ§­_µX6î½
¨µº À² ¿ºº¬i¨¬÷N?«?¹H:½K¦µ¿ pov(2.0) §­¿wí¬3ù¨¬6î_µXª£¦¹¨¹À
 3«¿ºº¬{ôe«¶¹ ' §¬6îDy½8´ H:½À%0ª·¿QUÃcSS8«ï¥µ¬¿;õr"«ï¥µ¬' §­X
6îû½¿/#¿¬ 2 OeÃ_í§¬6î­ª¸4ª¹À¤²¸¿ºº¬OeÃ_¨«=%ª ½'¹÷N?¬¹5*¹¨Ö
5º¹À
6ó31ú

ÈT§­¿;õXÃV½Ú¹{ôe«?¦¿PÃ]S·ÿ' +Ë8ªD¸#r"½&³
¨E§(/¹!$½6 À¬!$§­·«¿]caÃV¨¦ r"½U¿ «4¦ û6¹¶«¦¹À«¿¬!$¨;¬¤¬{ôe¨;«¿öN?¨öN?$Y¬«¶¹7½Vª¿¬ÓB ½ Ò8§¿sD½3¦wí¬3ù
«¶¹ ¨¬ ç½Vª£ À¬H:¿È!$­¿«¶¹7§X~ ½¿wí¬{3ù¨¬6î¬Ð§µ¿;¬{ôe¨ç±¦X~½ À·«¿QUÃcS«ï¥{ôe¨;õr"«ï¥{ôe­¿¬ û¬6î«¦¿¤«º¦¹¨º À¬¨­¿ºº½6 8«Ú¹¨§¿R«X~ ¶¯¿wí¬3ù¨X6î½¤{ôe)·º¹92{½¨ ¼º¹À\M­¿º·½©¬¶«3³Y¼¹¨§~ Pp¹½0±¹Û¹À
ÈJ9«­¿r"ft>@<à¬G9óÚ.ÈN
i IPAL ¿[[NJ9h¬N¿Ê.u¬ CD- Ê.uÆ94 04Ç½ÝÚ¦ ¡ ¨½ë²Àtr[1] E. Agirre and G. Rigau. A proposal for word
sense disambiguation using conceptual distance.
In Proceedings of 1st International Conference on
Recent Advances in Natural Language Processing,
1995.
[2] Y. Karov and S. Edelman. Learning similaritybased word sense disambiguation. In Proceedingsof the Fourth Workshop on Very Large Corpora,
1996.
[3] Y. Niwa and Y. Nitta. Co-occurrence vectors
from corpora vs. distance vectors from dictionaries. In Proc. COLING 94, Vol. 1, pp. 304{309,1994.
[4] P. Resnik. Using information content to evaluatesemantic similarity in a taxonomy. In Proceedingsof the 14th International Joint Conference on Arti cial Intelligence, Vol. 1, pp. 448{453, 1995.

[5] 3l, þ. PÃ]S·ÿ' D¸#;õr"«ï¥ ¨iI5«¹7. r"fèàJ9"Z 0HNf J9à, 96-NL-116,
96(114):73{78, Nov. 1996.