CRF¸Çͭɽ¸½¥Ç¡¼¥¿

¸Çͭɽ¸½Ãê½Ð¤Î¾ì¹ç

  • ÆüËܸì¸Çͭɽ¸½
    • crf++¤Ë´Þ¤Þ¤ì¤Æ¤¤¤ëexample/JapaneseNE¤ò»²¹Í¤Ë¤¹¤ë
    • $crf_learn -p2 -f 3 -c 4.0 template train.data model
      	-p 2  ¤Î°ÕÌ£ ¡¡Æ°ºî¤µ¤»¤ë¥Þ¥·¥ó¤¬Ê£¿ô¤ÎCPU¤ò»ý¤Ã¤Æ¤¤¤ë¤Ê¤é¡¢
      	¤½¤Î¸Ä¿ô¤ò¤¢¤¿¤¨¤ë¤È®¤¯¤Ê¤ë¡£
      	-f 3¡¡¤Î°ÕÌ£¡¡ÁÇÀ­¤òÍøÍѤ¹¤ë¤¿¤á¤Î½Ð¸½²ó¿ô¤ÎïçÃ͡ʤ·¤­¤¤¤Á¡Ë¡¡
      	¤³¤Î¾ì¹ç3²ó¤è¤ê¾®¤µ¤¯¤Ê¤¤ÁÇÀ­¤òÍøÍÑ¡£¥Ç¥Õ¥©¥ë¥È¤Ï1²ó¡£
      	-c 4.0¡¡¤Î°ÕÌ£ ¡¡CRF¤Î¥ª¥×¥·¥ç¥ó¤ÇÃͤòÂ礭¤¯¤¹¤ë¤È²á³Ø½¬¤¹¤ë·¹¸þ¤Ë¤¢¤ë¡£
      	¥Ð¥é¥ó¥¹¤è¤¯ÀßÄꤹ¤ë¡£
    • $ crf_test -m model M_test.dat
      • train¥Ç¡¼¥¿¤Îºî¤êÊý¤¬·ÁÂÖÁDzòÀϤȰۤʤ롣ñ¸ì\t¾ðÊó1\t¾ðÊó2\tIOB2¥¿¥°
      • ¸½ºß¤Ï¡¢¶¯°ú¤Ëmecab¤Î½ÐÎϤò¤½¤ì¤Ã¤Ý¤¯ÊѤ¨¤Æ¤¤¤ë¡£
        	¡¡ 8 ¡¡-µ­¹æ-¶õÇò O
        	Âçµ×ÊÝ 43 Âçµ×ÊÝ-̾»ì-¸Çͭ̾»ì-¿Í̾-À« B-HUMAN
        	¡¡ 8 ¡¡-µ­¹æ-¶õÇò I-HUMAN
        	¤¨ 2 ¤¨-´¶Æ°»ì I-HUMAN
        	¡û 4 ¡û-µ­¹æ-°ìÈÌ I-HUMAN
        	¤³ 38 ¤³-̾»ì-°ìÈÌ I-HUMAN
        	¡¡ 8 ¡¡-µ­¹æ-¶õÇò O
        	 O
  • IOB¥¿¥°
    • Æ⦡¢³°Â¦¡¢»Ï¤Þ¤ê¤Îµ­¹æ
      	#B: ¥Á¥ã¥ó¥¯¤ÎÀèƬ
      	#I : ¥Á¥ã¥ó¥¯¤ÎÆâÉô
      	#O: ¥Á¥ã¥ó¥¯¤Î³°Éô
      	#E: ¥Á¥ã¥ó¥¯¤ÎËöÈø
      	#S: °ì¤Ä¤Î¸ì¤Ç¥Á¥ã¥ó¥¯¤ò¹½À®¤¹¤ë
  • IOB2¥¿¥°
    • IOB¤òÉÕ¤±¤ë¤À¤±¤Ç¤Ê¤¯¡¢¾ÜºÙ¾ðÊó¡ÊLocation,Human¤Ê¤É¡Ë¤òÉÕÍ¿
      	#B: B-Location B-Human
      	#I : I-Location I-Human
      	#O: O ¤Ï¤½¤Î¤Þ¤Þ

·ÁÂÖÁDzòÀϤγؽ¬¤Î¾ì¹ç

  • ³Ø½¬¥³¥Þ¥ó¥É crf_learn
    • crf_learn -a MIRA template train.data model
  • ¥Æ¥¹¥È¥³¥Þ¥ó¥É crf_test
    • crf_test -m model test.data
  • ¼ÂºÝ¤Î¥Õ¥¡¥¤¥ë
  • ÍøÍѤ¹¤ë¥Õ¥¡¥¤¥ë¤È¤½¤ÎÃæ¿È
    • template
    • train.data
    • test.data¡¡È¾³Ñ¥¹¥Ú¡¼¥¹¶èÀÚ¤ê
      	Rockwell NNP B-NP
      	International NNP I-NP
      	Corp. NNP I-NP
      	's POS B-NP
      	Tulsa NNP I-NP
      	unit NN I-NP
      	said VBD B-VP
      	it PRP B-NP
      	signed VBD B-VP
    • $ cat train.data
      	He        PRP  B-NP
      	reckons   VBZ  B-VP
      	the       DT   B-NP
      	current   JJ   I-NP
      	account   NN   I-NP
      	deficit   NN   I-NP
      	will      MD   B-VP
      	narrow    VB   I-VP
      	to        TO   B-PP
      	only      RB   B-NP
      	#         #    I-NP
      	1.8       CD   I-NP
      	billion   CD   I-NP
      	in        IN   B-PP
      	September NNP  B-NP
      	.         .    O
      	
      	He        PRP  B-NP
      	reckons   VBZ  B-VP
    • $ cat template
      • template¤È¤Ï¡¦¡¦¡¦
        	# Unigram
        	U00:%x[-2,0]
        	U01:%x[-1,0]
        	U02:%x[0,0]
        	U03:%x[1,0]
        	U04:%x[2,0]
        	U05:%x[-1,0]/%x[0,0]
        	U06:%x[0,0]/%x[1,0]
        	
        	U10:%x[-2,1]
        	U11:%x[-1,1]
        	U12:%x[0,1]q
        	U13:%x[1,1]
        	U14:%x[2,1]
        	U15:%x[-2,1]/%x[-1,1]
        	U16:%x[-1,1]/%x[0,1]
        	U17:%x[0,1]/%x[1,1]
        	U18:%x[1,1]/%x[2,1]
        	
        	U20:%x[-2,1]/%x[-1,1]/%x[0,1]
        	U21:%x[-1,1]/%x[0,1]/%x[1,1]
        	U22:%x[0,1]/%x[1,1]/%x[2,1]
    • $ crf_learn -a MIRA template train.data model
      	CRF++: Yet Another CRF Tool Kit
      	Copyright(C)2005-2007 Taku Kudo, All rights reserved.
      	
      	reading training data:
      	Done!0.00 s
      	
      	Number of sentences: 2
      	Number of features:  1800
      	Number of thread(s): 1
      	Freq:                1
      	eta:                 0.00010
      	C:                   1.00000
      	shrinking size:      20
      	Algorithm:           MIRA
      	
      	iter=0 terr=0.66667 serr=0.50000 act=2 uact=0 obj=0.30126 kkt=12.00000
      	iter=1 terr=0.16667 serr=0.50000 act=2 uact=0 obj=0.36494 kkt=2.84937
      	iter=2 terr=0.00000 serr=0.00000 act=2 uact=0 obj=0.36494 kkt=0.00000
      	iter=3 terr=0.00000 serr=0.00000 act=2 uact=0 obj=0.36494 kkt=0.00000
      	
      	Done!0.00 s
    • $ crf_test -m model test.data
      	He      PRP     B-NP    B-NP
      	reckons VBZ     B-VP    B-VP
      	the     DT      B-NP    B-NP
      	current JJ      I-NP    I-NP
      	account NN      I-NP    I-NP
      	deficit NN      I-NP    I-NP
      	will    MD      B-VP    B-VP
      	narrow  VB      I-VP    I-VP
      	to      TO      B-PP    B-PP
      	only    RB      B-NP    B-NP
      	#       #       I-NP    I-NP
      	1.8     CD      I-NP    I-NP
      	billion CD      I-NP    I-NP
      	in      IN      B-PP    B-PP
      	September       NNP     B-NP    B-NP
      	.       .       O       O
      	
      	He      PRP     B-NP    B-NP
      	reckons VBZ     B-VP    B-VP

ÌÚ¼¥¼¥ßÀ¸¸ÂÄê

ÊÔ½¸²èÌÌ
¥¼¥ßÀ¸
2021-2022ǯÅÙÀ¸(14´ü)
°¤ÉôÍÚÂçÆâñ¥
²¬ÅÄ°¼²»¶½À±ÍÛ
³á±ï²ÏÌîͳÌï
º´¡¹ÌÚô¥º´¡¹ÌÚÈþÇÈ
ßÀÅÄϵ®×¢µÈϵ®
Æ£°æ°ì»ÖÆ£ÅĽ¡¿¿
2020-2021ǯÅÙÀ¸(13´ü)
¾®ß·¿¿ô¥³Þ¸¶Í­¿¿
²ÃÆ£ÀµÃè³÷ÅÄÌöÅÍ
ºä¼Íã½»µÈ¿¿Æà
¹âÌîÂç²ÏÃæ°æÍÕ·î
±ÊÞ¼·ÊÍ´Ê¿´ÛºÚ¡¹»Ò
2019-2020ǯÅÙÀ¸(12´ü)
Âç°²¶³Ê¿--
¶áÆ£ÂÀͺÀ¶¿åÈþΤ
Ãæ¼²ÄÎçÊ¡»³³èµ¯
Ê¡²ÈÍ´µªÁ¥±ÛÅ·ºÌ
Æ°áΤ»³²¼²À·î
ºäËÜÎÃÂÀÅÚ²°ºÌ²Æ
2018-2019ǯÅÙÀ¸(ÉÔºß)
SEA-NAÂåɽ¼èÄùÌò
Ê¿²ìľµ±²£»³è½²Ö
½»µÈ¼Âµ§¼¼¶¶ÏºÈ
2017-2018ǯÅÙÀ¸(11´ü)
ÀйõÛÙÆà°ìµÜÂó³¤
µµ°æ³¤½®º´Æ£ÛÙ
º´Æ£Í­´õÉ°¿¹Âó¿¿
Æ£Ëܼë²Æ¥Û¥ï¥¤¥È¥¸¥Ë¡¼
ÁýÅÄÍ¥ºîëÆâ·òÂÀ
2016-2017ǯÅÙÀ¸(10´ü)
°ËÆ£¤ß¤­²¬Åç·ò¸ç
¾®À¾ÀãÍÕÍ´ÀîÂÙµ±
ÎëÌÚͤºÚÂçÌçÂó»Ë
ÅÄƬ¤ï¤«¤Ð¸ÍÅèºéÊæ
Ãæ¼ÃÒµ®À¾ÌîůÀ¸
²£»³Í´²ÌÀî´ßÍ´²Ì
2015-2016ǯÅÙÀ¸(09´ü)
Àõ²ì¼·³¤¾®ÎÓ¿¿ºÚ
À¾Â¼°Ë±ûËÙ¹¾ÃÎ̤
¿ËÀ¸°Ô´õ¼¾å¹ÀÂÀ
2014-2015ǯÅÙÀ¸(08´ü)
ÂçÀÐÀ¿ÂçÌÂÀϯ
²Ãƣ͵¼ùº´¡¹ÌÚº¸¶á
¹â¶¶Íýº»ÉðÅÄè½Êæ
»ûÅçÉñ»ÒȪ²ìÂç
»³ÅĽ¤À¤
2013-2014ǯÅÙÀ¸(07´ü)
²ÃÆ£»Ë¿¥¹©Æ£ÃÒ»Ò
º´¡¹ÌÚÍÕ»Ò»Ö³ù¼þ
¹â¶¶¸¼Î¶üâ¾¾æÆ
ÃæÈøÀéºéÃæÀîÎèºÚ
Ãæé®Âçµ®
2012-2013ǯÅÙÀ¸(06´ü)
±óÆ£À±ÃÏÂçÌî¼Óµ¨
³ùÅĤᤰ¤ßÌÚ²¼ÏÂÂç
ã·ÌÚÎò𺴡¹ÌÚÍÚ
º´Æ£Í¥»Ò¾Â߷ʸ¹á
¸Å²°¿¿ÍýµÈÅÄÃÒ¹°
2010-2011ǯÅÙÀ¸(05´ü)
°±¸¶»ËÉÒ°ËÆ£Â絯
°ËÆ£¤ß¤É¤ê±Êºäʸǵ
Æ£ÅĹҺÈÁ°Â¿ÂçÊå
¾¾ËÜÎÍͤµÜÄÅÍ­º»
»³ÅÄ°¡µ¨
2009-2010ǯÅÙÀ¸(04´ü)
´ßËÜδ»Ö·¦ÃÏͳ·Ã
»Ö²ìÀéÄáÄÅÅÄÍ­»Ò
»°±º¹©Ìï
2008-2009ǯÅÙÀ¸(03´ü)
°ÀÄŹ¯Í¤°æ¾å¤µ¤æ¤ê
Ë̺êͤ¼ù¹©Æ£Ï´²
¸ÅËóÍ¥²Öº´Àî¾´¹¨
º´Æ£Ä÷ÍÎÎëÌÚ°¡°á
Ãݸ¶´õÈþÆ£°æÍ¥ºî
ËÙ¸ø°ìËÙÆâ¾®¿¥
ÊÆß·¹¨»Ë
2007-2008ǯÅÙÀ¸(02´ü)
º´Æ£·òÂÀ¾å¼²Â¹°
±üÅÄ·¼µ®¾®ÌîÀ¿
Çò°æ¤«¤º¤ß¹â°æÍDzð
¿¹Ã«Î¼²ðÏ»ÅÏÍ­Íü·Ã
¼ãËÜůʿ
2006-2007ǯÅÙÀ¸(01´ü)
¿û°æ°´ÅÏÉô¸¬ÂÀϺ
Áêºä¿¿Â缲¿µ
±üÅí»Ò³Þ°æÌÔ
¾®ÎÓϹ¬óîÆ£¤¤¤Ä¤³
óîƣͺµªº´¡¹ÌÚËã̤
º´Æ£Æü²ÃÍùëËܵ®Ç·
ÆÁ¹¾Í¤²ðĹÎææûÊ¿
À¾Ëܤߤ椭ÎÓ³¨Î¤»Ò
ß·ÅÄÂçµ±