; verbose (-v: yes -v-: no) -v - ; keep intermediary files (-x: yes -x-: no) -x - ; flex rules (input file, binary format) ;-b (not specified, not doing actions with already created flex rules.) ; Word list (Optional if -b is specified. Otherwise N/A) (-I filename) ;-I (N/A) ; Output, lemmas of words in input (-I option) ;-O (N/A) ; word/lemma list -i greek3.ph ; extra file name affix -e ziggurat ; suffix only (-s: yes -s-: no) -s - ; make rules with infixes less prevalent(-A: yes -A-: no) -A - ; columns (1 or F or W=word,2 or B or L=lemma,3 or T=tags,0 or O=other) -n FBO ; max recursion depth when attempting to create candidate rule -Q 1 ; flex rules (output, binary format, can be left unspecified) ;-o (Not specified, autogenerated) ; temp dir (including separator at end!) -j tmp/ ; penalties to decide which rule survives (4 or 6 floating point numbers: R=>R;W=>R;R=>W;W=>W[;R=>N/A;W=>NA], where R=#right cases, W=#wrong cases, N/A=#not applicable cases, previous success state=>success state after rule application) -D 0.039035;-0.441248;0.889026;0.066674;0.039309;0.086126; ; compute parms (-p: yes -p-: no) -p ; expected optimal pruning threshold (only effective in combination with -XW) -C -1 ; tree penalty (-XC: constant -XD: more support is better -XE: higher entropy is better -XW: Fewer pattern characters other than wildcards is better) -X C ; current parameters (-P filename) -P parms.txt ; best parameters (-B filename) -B best_ziggurat.txt ; start training with minimal fraction of training pairs (-Ln: 0.0 < n <= 1.0) -L 0.017709 ; end training with maximal fraction of training pairs (-Hn: 0.0 < n <= 1.0) -H 1.000000 ; number of differently sized fractions of trainingdata (natural number) -K 20 ; number of iterations of training with same fraction of training data when fraction is minimal (positive number) -N 100.000000 ; number of iterations of training with same fraction of training data when fraction is maximal (positive number) -M 10.000000 ; competition function (deprecated) ;-f (N/A) ; redo training after homographs for next round are removed (-R: yes -R-: no) ;-R - (N/A) ; max. pruning threshold to evaluate -c 5 ; test with the training data (-T: yes -T-: no) -T ; test with data not used for training (-t: yes -t-: no) -t ; create flexrules using full training set (-F: yes -F-: no) -F ; Number of clusters found in word/lemma list: 45779 ; Number of lines found in word/lemma list: 564700 ; Evaluation: ; ----------- ; Lemmatization results for all data in the training set. ; For pruning threshold 0 there may be no errors (diff%%). ; prun. thrshld. 0 1 2 3 4 5 ; rules 59706.000000 34411.000000 10938.000000 6868.000000 5244.000000 4355.000000 ; rules% 10.573048 6.093678 1.936958 1.216221 0.928635 0.771206 ; same% 94.471755 91.312910 88.544714 87.492651 86.817425 86.325837 ; ambi1% 2.752612 2.794404 2.472995 2.376837 2.342483 2.268284 ; ambi2% 2.752612 2.647600 2.198335 2.096511 2.060740 2.007615 ; ambi3% 0.023021 0.014167 0.003188 0.000177 0.000000 0.000000 ; diff% 0.000000 3.230919 6.780769 8.033823 8.779352 9.398265 ; same%stdev 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ; ambi1%stdev 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ; ambi2%stdev 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ; ambi3%stdev 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ; diff%stdev 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ; ;Evaluation of prediction of ambiguity (whether a word has more than one possible lemma) ;--------------------------------------------------------------------------------------- ; amb.rules% 5.528245 5.593058 4.934478 4.730653 4.682486 4.537808 ; false_amb% 0.000000 0.721091 0.761289 0.718435 0.760581 0.687622 ; false_not_amb% 0.000000 0.656278 1.355056 1.516026 1.606340 1.678059 ; true_amb% 5.528245 4.871967 4.173189 4.012219 3.921905 3.850186 ; true_not_amb% 94.471755 93.750664 93.710466 93.753320 93.711174 93.784133 ; precision 1.000000 0.771595 0.732683 0.736310 0.720532 0.736817 ; recall 1.000000 0.881286 0.754885 0.725767 0.709430 0.696457 ; Evaluation: ; ----------- ; Lemmatization results for data that is not part of the training data. ; prun. thrshld. 0 1 2 3 4 5 ; rules 58901.800000 33958.800000 10808.600000 6812.600000 5212.200000 4327.000000 ; rules% 10.578510 6.098855 1.941178 1.223514 0.936089 0.777111 ; same% 85.424004 85.619093 85.905394 85.596291 85.277053 84.935012 ; ambi1% 2.564038 2.467760 2.265069 2.161190 2.123185 2.135853 ; ambi2% 2.082647 2.059844 1.976235 1.910360 1.882490 1.882490 ; ambi3% 0.005067 0.002534 0.000000 0.002534 0.000000 0.000000 ; diff% 9.924244 9.850769 9.853303 10.329626 10.717272 11.046644 ; same%stdev 0.826480 0.830439 0.933204 0.962539 1.002514 1.028687 ; ambi1%stdev 0.272754 0.204068 0.200208 0.201529 0.204925 0.199445 ; ambi2%stdev 0.186769 0.144487 0.188432 0.165448 0.170775 0.204926 ; ambi3%stdev 0.006812 0.005468 0.000000 0.005909 0.000000 0.000000 ; diff%stdev 0.884649 0.881438 0.878746 0.855822 0.883543 0.929821 ; ;Evaluation of prediction of ambiguity (whether a word has more than one possible lemma) ;--------------------------------------------------------------------------------------- ; amb.rules% 5.128075 4.938053 4.514936 4.345182 4.304644 4.279308 ; false_amb% 0.235628 0.220426 0.169753 0.136816 0.136816 0.152018 ; false_not_amb% 0.243229 0.233094 0.278700 0.304036 0.304036 0.304036 ; true_amb% 0.841166 0.851301 0.805696 0.780359 0.780359 0.780359 ; true_not_amb% 19.688870 19.704072 19.754744 19.787681 19.787681 19.772480 ; precision 0.640927 0.658824 0.703540 0.740385 0.740385 0.719626 ; recall 0.775701 0.785047 0.742991 0.719626 0.719626 0.719626 ; ; Power law relating the number of rules in the decision tree to the number of examples in the training data ;---------------------------------------------------------------------------------------------------------- ; #rules = 2.179*N^0.769 1.306*N^0.766 0.787*N^0.718 0.657*N^0.697 0.587*N^0.685 0.527*N^0.680 ; Postscriptum ; The number of rules can be estimated from the number of training examples by ; a power law. See the last line in the table above, which is based on 7 ; different samples from the total available training data mass varying in size ; from 1.54 % to 98.56 %