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HW7 Extracting Arguments for %

HW7 Extracting Arguments for %. Ang Sun asun@cs.nyu.edu March 25, 2012. Outline. File Format Training Generating Training Examples Extracting Features Training of MaxEnt Models Decoding Scoring. File Format.

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HW7 Extracting Arguments for %

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  1. HW7 Extracting Arguments for % Ang Sun asun@cs.nyu.edu March 25, 2012

  2. Outline • File Format • Training • Generating Training Examples • Extracting Features • Training of MaxEnt Models • Decoding • Scoring

  3. File Format • Statistics Canada said service-industry <ARG1> output </ARG1> in August <SUPPORT> rose </SUPPORT> 0.4 <PRED class="PARTITIVE-QUANT"> % </PRED> from July .

  4. Generating Training Examples • Positive Example • Only one positive example for a sentence • The one with the annotation ARG1

  5. Generating Training Examples • Negative Examples • Two methods! • Method 1: consider any token that has one of the following POSs • NN 1150 • NNS 905 • NNP 205 • JJ 25 • PRP 24 • CD 21 • DT 16 • NNPS 13 • VBG 2 • FW 1 • IN 1 • RB 1 • VBZ 1 • WDT 1 • WP 1 Too many negative examples!

  6. Generating Training Examples • Negative Examples • Two methods! • Method 2: only consider head tokens

  7. Extracting Features f: candToken=output

  8. Extracting Features f: tokenBeforeCand=service-industry

  9. Extracting Features f: tokenAfterCand=in

  10. Extracting Features f: tokensBetweenCandPRED=in_August_rose_0.4

  11. Extracting Features f: numberOfTokensBetween=4

  12. Extracting Features f: exisitVerbBetweenCandPred=true

  13. Extracting Features f: exisitSUPPORTBetweenCandPred=true

  14. Extracting Features f: candTokenPOS=NN

  15. Extracting Features f: posBeforeCand=NN

  16. Extracting Features f: posAfterCand=IN

  17. Extracting Features f: possBetweenCandPRED=IN_NNP_VBD_CD

  18. Extracting Features f: BIOChunkChain= I-NP_B-PP_B-NP_B-VP_B-NP_I-NP

  19. Extracting Features f: chunkChain= NP_PP_NP_VP_NP

  20. Extracting Features f: candPredInSameNP=False

  21. Extracting Features f: candPredInSameVP=False

  22. Extracting Features f: candPredInSamePP=False

  23. Extracting Features f: shortestPathBetweenCandPred= NP_NP-SBJ_S_VP_NP-EXT

  24. Training of MaxEnt Model • Each training example is one line • candToken=output . . . . . class=Y • candToken=Canada . . . . . Class=N • Put all examples in one file, the training file • Use the MaxEnt wrapper or the program you wrote in HW5 to train your relation extraction model

  25. Decoding • For each sentence • Generate testing examples as you did for training • One example per feature line (without class=(Y/N)) • Apply your trained model to each of the testing examples • Choose the example with the highest probability returned by your model as the ARG1 • So there should be and must be one ARG1 for each sentence

  26. Scoring • As you are required to tag only one ARG1 for each sentence • Your system will be evaluated based on accuracy • Accuracy = #correct_ARG1s / #sentences

  27. Good Luck!

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