C:\my_study\ML\NLP\stanford-ner--->java -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -prop chinese.meal.fpp.prop

Invoked on Thu Mar  :: CST  with arguments: -prop chinese.meal.fpp.prop

usePrevSequences=true

useClassFeature=true

useTypeSeqs2=true

useSequences=true

wordShape=chris2useLC

useTypeySequences=true

useDisjunctive=true

noMidNGrams=true

serializeTo=ner-model.ser.gz

maxNGramLeng=

useNGrams=true

usePrev=true

useNext=true

maxLeft=

trainFile=chinese.meal.fpp.tsv

map=word=,answer=

useWord=true

useTypeSeqs=true

numFeatures =

Time to convert docs to feature indices: 0.0 seconds

numClasses:  [=O,=TIME,=QUANTITY,=UNIT,=FOOD]

numDocuments:

numDatums:

numFeatures:

Time to convert docs to data/labels: 0.0 seconds

numWeights:

QNMinimizer called on double function of  variables, using M = .

               An explanation of the output:

Iter           The number of iterations

evals          The number of function evaluations

SCALING        <D> Diagonal scaling was used; <I> Scaled Identity

LINESEARCH     [## M steplength]  Minpack linesearch

                   -Function value was too high

                   -Value ok, gradient positive, positive curvature

                   -Value ok, gradient negative, positive curvature

                   -Value ok, gradient negative, negative curvature

               [.. B]  Backtracking

VALUE          The current function value

TIME           Total elapsed time

|GNORM|        The current norm of the gradient

{RELNORM}      The ratio of the current to initial gradient norms

AVEIMPROVE     The average improvement / current value

EVALSCORE      The last available eval score

Iter ## evals ## <SCALING> [LINESEARCH] VALUE TIME |GNORM| {RELNORM} AVEIMPROVE EVALSCORE

Iter  evals  <D> [M 1.000E-1] 9.068E2 .04s |4.550E1| {4.995E-1} 0.000E0 -

Iter  evals  <D> [M 1.000E0] 6.222E2 .05s |3.525E1| {3.870E-1} 2.287E-1 -

Iter  evals  <D> [M 1.000E0] 2.386E2 .07s |5.406E1| {5.935E-1} 9.334E-1 -

Iter  evals  <D> [M 1.000E0] 9.082E1 .08s |1.571E1| {1.724E-1} 2.246E0 -

Iter  evals  <D> [M 1.000E0] 7.031E1 .10s |1.181E1| {1.297E-1} 2.379E0 -

Iter  evals  <D> [M 1.000E0] 5.308E1 .11s |1.025E1| {1.125E-1} 2.681E0 -

Iter  evals  <D> [1M 2.740E-1] 2.988E1 .14s |7.586E0| {8.328E-2} 4.193E0 -

Iter  evals  <D> [1M 1.292E-1] 2.234E1 .16s |6.471E0| {7.105E-2} 4.949E0 -

Iter  evals  <D> [1M 1.801E-1] 1.615E1 .18s |5.573E0| {6.118E-2} 6.127E0 -

Iter  evals  <D> [1M 1.815E-1] 1.218E1 .24s |4.477E0| {4.915E-2} 7.346E0 -

Iter  evals  <D> [1M 3.119E-1] 8.873E0 .30s |4.694E0| {5.154E-2} 6.912E0 -

Iter  evals  <D> [1M 4.760E-1] 6.621E0 .31s |2.092E0| {2.296E-2} 3.504E0 -

Iter  evals  <D> [M 1.000E0] 6.093E0 .32s |1.906E0| {2.092E-2} 1.390E0 -

Iter  evals  <D> [M 1.000E0] 5.844E0 .33s |9.067E-1| {9.955E-3} 1.103E0 -

Iter  evals  <D> [M 1.000E0] 5.721E0 .33s |5.774E-1| {6.339E-3} 8.279E-1 -

Iter  evals  <D> [M 1.000E0] 5.660E0 .34s |3.535E-1| {3.881E-3} 4.279E-1 -

Iter  evals  <D> [M 1.000E0] 5.640E0 .35s |1.946E-1| {2.137E-3} 2.961E-1 -

Iter  evals  <D> [M 1.000E0] 5.632E0 .36s |7.832E-2| {8.599E-4} 1.868E-1 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .38s |3.559E-2| {3.907E-4} 1.163E-1 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .39s |2.149E-2| {2.359E-4} 5.758E-2 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .41s |1.027E-2| {1.128E-4} 1.758E-2 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .42s |3.631E-3| {3.986E-5} 8.218E-3 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .44s |1.629E-3| {1.789E-5} 3.791E-3 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .45s |9.548E-4| {1.048E-5} 1.596E-3 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .45s |5.724E-4| {6.284E-6} 5.196E-4 -

Iter  evals  <D> [M 1.000E0] 5.631E0 .47s |1.578E-4| {1.732E-6} 1.686E-4 -

QNMinimizer terminated due to average improvement: | newest_val - previous_val | / |newestVal| < TOL

Total time spent in optimization: .49s

CRFClassifier training ... done [0.6 sec].

Serializing classifier to ner-model.ser.gz... done.

C:\my_study\ML\NLP\stanford-ner--->java -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier ner-model.ser.gz -testFile chinese.meal.fpp.test.tsv

Invoked on Thu Mar  :: CST  with arguments: -loadClassifier ner-model.ser.gz -testFile chinese.meal.fpp.test.tsv

testFile=chinese.meal.fpp.test.tsv

loadClassifier=ner-model.ser.gz

Loading classifier from ner-model.ser.gz ... done [0.1 sec].

我      O       O

今天    O       O

晚上    TIME    TIME

吃      O       O

了      O       O

两      QUANTITY        QUANTITY

盘      UNIT    UNIT

回锅肉  FOOD    FOOD

CRFClassifier tagged  words in  documents at 88.89 words per second.

         Entity P       R       F1      TP      FP      FN

           FOOD 1.0000  1.0000  1.0000

       QUANTITY 1.0000  1.0000  1.0000

           TIME 1.0000  1.0000  1.0000

           UNIT 1.0000  1.0000  1.0000

         Totals 1.0000  1.0000  1.0000