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Extracting Arguments for Relation Detection Model Training

Learn about file format, training examples generation, feature extraction, MaxEnt model training, decoding, and scoring for relation extraction. Understand methods for generating positive and negative training examples and extracting relevant features. Explore training the MaxEnt model and decoding sentence examples for relation detection. Build an accurate system for tagging ARG1 in sentences.

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Extracting Arguments for Relation Detection Model Training

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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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