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| acl_2006 applications | |
| acl_2006 asian_language_processing | |
| acl_2004 conversational_spoken_language_processing | |
| acl_2006 coreference | |
| acl_2004 coreference_and_anaphora | |
| acl_2005 corpus_annotation | |
| acl_2006 dialogue | |
| acl_2004 dialogue_systems | |
| acl_2005 discourse | |
| acl_2006 discourse |
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| ~/tdf/exper % Rscript ../sigtest.r *.test.* | |
| *** Results with bootstrapped CI's | |
| f mae ci.conf ci.V2 ci.V3 | |
| 1 meta.test.last.prediction.4.000e-02 353.8108 0.95 314.0861 394.0680 | |
| 2 meta.test.single.prediction.4.000e-02 334.8699 0.95 297.5722 373.5800 | |
| 3 meta.test.singlenogap.prediction.4.000e-02 335.2703 0.95 299.4178 370.9729 | |
| 4 meta.test.ts.prediction.1.600e-03 335.3140 0.95 298.3792 370.9963 | |
| 5 text.test.last.prediction.2.000e-01 342.7077 0.95 305.0201 380.5768 | |
| 6 text.test.single.prediction.4.000e-02 295.0461 0.95 261.3029 329.7087 |
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| dataset | NBER | ACL | | |
| response | log(#downloads) | 1{ #citations >= 5 } | | |
| model (GLM variant) | gaussian/squared loss | linear logistic loss | | |
| | aka linear regression | aka MaxEnt | | |
| interp of feat weight=1 | e times more downloads | e times higher prob of high-citation class | | |
| metric1 | MAE | Acc (BalAcc?) | | |
| metric2, maybe dont use | tau | tau | |
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| 325 !! !!! !!!! !!!!! !!!!!! !!!!!!! !!!!!!!! !!!!!!!!! !!!!!!!.. !!.. !!.... !!: !!?? !" !' !. !... !: !? ", ". ": #1 #2 #2010 #39 #4 #8217 #ui #ww $1 $10 $100 $1000 $188 $2 $20 $200 $25 $32 $379 $5 && '" '' ($149 ($169 (( ((( (((((((((((((((((((((((((((((((( (: (= (@ (^_^) (¬_¬ )( ))) ): *)) ** **awwyyy *] +22 ," ,... -& -- --- ----- ------> ----> ---->>> ---> --> -6 -> -_- -__- -___- .! ." .' ., .. ... ..." .... ..... ...... ....... ........ ............ ...: ..: .: .?!! 0-1 0.00 00 04:45 09 1,000 1-0 1/2 10 10.27 10.4 10/26 10/27- 10/27/2010 10/30/10 100% 100,000 10093 101 101.1 106 107.5 109a 10:45 10:55 10¢ 11 11/01/10 11:30 12 12.99 1200 1221 13 13% 13.94 14:14 15 15.7 15/30 16 161 17 17% 175 1793 17:27 18 1895 18:1 1980 1995 2+3 2.0 2.3 2.5 2/3 20 20% 2008 2010 2011 2014 2020 21 2221 23 23.0 257 26 27 28 29 29.676 30 30% 300 31 31.1 33.1 330 35 360 3:00 4,900 4-8 4.25 40 40% 401 45% 465.00 48 5'1 50 500 5000 516 52 53 55 56.3 57 5o 6-8 6.05 6.95 60-80% 63 640 6501 67 6:30 6:40 70 7046614311 75 76 7 |
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| 325 !! !!! !!!! !!!!! !!!!!! !!!!!!! !!!!!!!! !!!!!!!!! !!!!!!!.. !!.. !!.... !!: !!?? !" !' !. !... !: !? ", ". ": #1 #2 #2010 #39 #4 #8217 #ui #ww $1 $10 $100 $1000 $188 $2 $20 $200 $25 $32 $379 $5 && '" '' ($149 ($169 (( ((( (((((((((((((((((((((((((((((((( (: (= (@ (^_^) (¬_¬ )( ))) ): *)) ** **awwyyy *] +22 ," ,... -& -- --- ----- ------> ----> ---->>> ---> --> -6 -> -_- -__- -___- .! ." .' ., .. ... ..." .... ..... ...... ....... ........ ............ ...: ..: .: .?!! 0-1 0.00 00 04:45 09 1,000 1-0 1/2 10 10.27 10.4 10/26 10/27- 10/27/2010 10/30/10 100% 100,000 10093 101 101.1 106 107.5 109a 10:45 10:55 10¢ 11 11/01/10 11:30 12 12.99 1200 1221 13 13% 13.94 14:14 15 15.7 15/30 16 161 17 17% 175 1793 17:27 18 1895 18:1 1980 1995 2+3 2.0 2.3 2.5 2/3 20 20% 2008 2010 2011 2014 2020 21 2221 23 23.0 257 26 27 28 29 29.676 30 30% 300 31 31.1 33.1 330 35 360 3:00 4,900 4-8 4.25 40 40% 401 45% 465.00 48 5'1 50 500 5000 516 52 53 55 56.3 57 5o 6-8 6.05 6.95 60-80% 63 640 6501 67 6:30 6:40 70 7046614311 75 76 7 |
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| Gold no_suffixes/model.pred ==> model_base/m.pred | |
| @ @ciaranyree @ @ | |
| O it O O | |
| V was V V | |
| P on P P | |
| N football N N | |
| N wives N N | |
| , , , , | |
| IMPROVE $ one O $ | |
| P of P P |
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| Gold then model_base/m.pred then include_noah/model.pred | |
| @ @ciaranyree @ @ | |
| O it O O | |
| V was V V | |
| P on P P | |
| N football N N | |
| N wives N N | |
| , , , , | |
| REGRESS $ one $ O | |
| P of P P |
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| +-zsh:400> paste ../blitz2011/data/newsplit/dev.goldtags.tab no_suffixes/model.pred model_base/m.pred | |
| +-zsh:400> tabawk '!$1{print ""; next} {change=($4!=$6); improve=$2==$6; regress=$2==$4; print !change ? "" : improve ? "IMPROVE" : regress ? "REGRESS" : "", $2,$1,$4,$6}' | |
| @ @ciaranyree @ @ | |
| O it O O | |
| V was V V | |
| P on P P | |
| N football N N | |
| N wives N N | |
| , , , , | |
| IMPROVE $ one O $ |
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| +-zsh:399> paste ../blitz2011/data/newsplit/dev.goldtags.tab no_suffixes/model.pred model_base/m.pred | |
| +-zsh:399> tabawk '!$1{print ""; next} {change=($4!=$6); improve=$2==$6; regress=$2==$4; print !change ? "" : improve ? "IMPROVE" : "REGRESS", $2,$1,$4,$6}' | |
| @ @ciaranyree @ @ | |
| O it O O | |
| V was V V | |
| P on P P | |
| N football N N | |
| N wives N N | |
| , , , , | |
| IMPROVE $ one O $ |
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| +-zsh:398> paste ../blitz2011/data/newsplit/dev.goldtags.tab no_suffixes/model.pred model_base/m.pred | |
| +-zsh:398> tabawk '!$1{print ""; next} {change=($4!=$6); improve=$2==$6; print !change ? "" : improve ? "IMPROVE" : "REGRESS", $2,$1,$4,$6}' | |
| @ @ciaranyree @ @ | |
| O it O O | |
| V was V V | |
| P on P P | |
| N football N N | |
| N wives N N | |
| , , , , | |
| IMPROVE $ one O $ |