Difference between revisions of "User:Francis Tyers/TLH"
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==Tarea 1== |
==Tarea 1== |
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<pre> |
<pre> |
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for x in `seq 0 9`; do |
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⚫ | |||
cooked2lex.pl < LexEsp-train-$x.cooked > train-$x.lex |
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cooked2ngram.pl < LexEsp-train-$x.cooked > train-$x.ngrams |
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cooked2raw.pl < LexEsp-$x.cooked > LexEsp-$x.raw |
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t3 train-$x.ngrams train-$x.lex < LexEsp-$x.raw > LexEsp-$x.t3 |
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⚫ | |||
cat output |
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done |
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$ cat output |
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418 sentences |
418 sentences |
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LexEsp-0.t3 9470 397 95.976% |
LexEsp-0.t3 9470 397 95.976% |
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418 sentences |
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LexEsp-1.t3 9290 410 95.773% |
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418 sentences |
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LexEsp-2.t3 9199 430 95.534% |
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418 sentences |
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LexEsp-3.t3 9264 440 95.466% |
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418 sentences |
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LexEsp-4.t3 9164 448 95.339% |
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418 sentences |
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LexEsp-5.t3 8908 397 95.733% |
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418 sentences |
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LexEsp-6.t3 8968 418 95.547% |
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418 sentences |
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LexEsp-7.t3 9334 414 95.753% |
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418 sentences |
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LexEsp-8.t3 9693 478 95.300% |
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417 sentences |
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LexEsp-9.t3 9434 405 95.884% |
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</pre> |
</pre> |
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Revision as of 12:37, 5 April 2008
Tarea 1
for x in `seq 0 9`; do cooked2lex.pl < LexEsp-train-$x.cooked > train-$x.lex cooked2ngram.pl < LexEsp-train-$x.cooked > train-$x.ngrams cooked2raw.pl < LexEsp-$x.cooked > LexEsp-$x.raw t3 train-$x.ngrams train-$x.lex < LexEsp-$x.raw > LexEsp-$x.t3 evaluate.pl LexEsp-$x.cooked LexEsp-$x.t3 >> output cat output done $ cat output 418 sentences LexEsp-0.t3 9470 397 95.976% 418 sentences LexEsp-1.t3 9290 410 95.773% 418 sentences LexEsp-2.t3 9199 430 95.534% 418 sentences LexEsp-3.t3 9264 440 95.466% 418 sentences LexEsp-4.t3 9164 448 95.339% 418 sentences LexEsp-5.t3 8908 397 95.733% 418 sentences LexEsp-6.t3 8968 418 95.547% 418 sentences LexEsp-7.t3 9334 414 95.753% 418 sentences LexEsp-8.t3 9693 478 95.300% 417 sentences LexEsp-9.t3 9434 405 95.884%
Tarea 2
$ for i in `seq 1 9`; do cat LexEsp-[1-$i].cooked > LexEsp-ejecucion$i.cooked; cooked2lex.pl < LexEsp-ejecucion$i.cooked > train.$i.lex; cooked2ngram.pl < LexEsp-ejecucion$i.cooked > train.$i.ngrams; t3 train.$i.ngrams train.$i.lex < LexEsp-0.raw > LexEsp-0.$i.t3; evaluate.pl LexEsp-0.cooked LexEsp-0.$i.t3 >> output ; done $ wc -l LexEsp-ejecucion*.cooked 418 LexEsp-ejecucion1.cooked 836 LexEsp-ejecucion2.cooked 1254 LexEsp-ejecucion3.cooked 1672 LexEsp-ejecucion4.cooked 2090 LexEsp-ejecucion5.cooked 2508 LexEsp-ejecucion6.cooked 2926 LexEsp-ejecucion7.cooked 3344 LexEsp-ejecucion8.cooked 3761 LexEsp-ejecucion9.cooked $ cat output 418 sentences LexEsp-0.1.t3 8948 919 90.686% 418 sentences LexEsp-0.2.t3 9155 712 92.784% 418 sentences LexEsp-0.3.t3 9275 592 94.000% 418 sentences LexEsp-0.4.t3 9313 554 94.385% 418 sentences LexEsp-0.5.t3 9366 501 94.922% 418 sentences LexEsp-0.6.t3 9391 476 95.176% 418 sentences LexEsp-0.7.t3 9419 448 95.460% 418 sentences LexEsp-0.8.t3 9444 423 95.713% 418 sentences LexEsp-0.9.t3 9470 397 95.976%
Tarea 3
$ for i in `seq 1 10`; do t3 -l $i train.ngrams train.lex < LexEsp-0.raw > LexEsp-0.l$i.t3; evaluate.pl LexEsp-0.cooked LexEsp-0.l$i.t3 >> output.l; done $ cat output.l 418 sentences LexEsp-0.l1.t3 9411 456 95.379% 418 sentences LexEsp-0.l2.t3 9466 401 95.936% 418 sentences LexEsp-0.l3.t3 9492 375 96.199% 418 sentences LexEsp-0.l4.t3 9490 377 96.179% 418 sentences LexEsp-0.l5.t3 9473 394 96.007% 418 sentences LexEsp-0.l6.t3 9477 390 96.047% 418 sentences LexEsp-0.l7.t3 9473 394 96.007% 418 sentences LexEsp-0.l8.t3 9470 397 95.976% 418 sentences LexEsp-0.l9.t3 9470 397 95.976% 418 sentences LexEsp-0.l10.t3 9470 397 95.976%
Tarea 4
$ prepare-corpus.sh LexEsp_Etq_Larga.cooked 4179 sentences 256 tags 16481 types 96961 tokens 1 15735 95.474% 69045 71.209% 2 689 4.181% 22621 23.330% 3 51 0.309% 4315 4.450% 4 3 0.018% 151 0.156% 5 3 0.018% 829 0.855% Mean ambiguity A=1.361176 Entropy H(p)=5.488119 $ cooked2lex.pl < LexEsp_Etq_Larga-train-0.cooked > train.larga.lex 3761 sentences 254 tags 15431 types 87094 tokens 1 14742 95.535% 62581 71.855% 2 637 4.128% 20044 23.014% 3 47 0.305% 3620 4.156% 4 2 0.013% 96 0.110% 5 3 0.019% 753 0.865% Mean ambiguity A=1.351161 Entropy H(p)=5.485330 $ cooked2ngram.pl < LexEsp_Etq_Larga-train-0.cooked > train.larga.ngrams $ cooked2raw.pl LexEsp_Etq_Larga-0.cooked > LexEsp_Etq_Larga-0.raw $ cooked2raw.pl < LexEsp_Etq_Larga-0.cooked > LexEsp_Etq_Larga-0.raw $ t3 train.larga.ngrams train.larga.lex < LexEsp_Etq_Larga-0.raw > LexEsp_Etq_Larga-0.t3 [ 4 ms::1] [ 4 ms::1] Trigram POS Tagger (c) Ingo Schröder, schroeder@informatik.uni-hamburg.de [ 4 ms::1] [ 2064 ms::1] model generated from 3761 sentences (thereof 43 one-word) [ 2064 ms::1] found 11283 uni-, 15044 bi-, and 18762 trigram counts for the boundary tag [ 12724 ms::1] computed smoothed transition probabilities [ 13512 ms::1] built suffix tries with 29924 lowercase and 6743 uppercase nodes [ 13532 ms::1] leaves/single/total LC: 7672 18878 29925 [ 13536 ms::1] leaves/single/total UC: 1320 4874 6744 [ 16329 ms::1] suffix probabilities smoothing done [theta 1.281e-02] [ 12249377 ms::1] done $ evaluate.pl LexEsp_Etq_Larga-0.cooked LexEsp_Etq_Larga-0.t3 418 sentences LexEsp_Etq_Larga-0.t3 9412 455 95.389%