NLP-Role-Labeler

Semantic Role Labeler in Natural Language Processing

Usage:
    Put file 'semantic_role_labeler.py' and folder 'data.wsj' in the same folder.
    -semantic_role_labeler.py
    -data.wsj
      |---ne                                  # ne : Named Entities.
      |---props                               # props : Target verbs and correct propositional arguments.
      |---synt.cha                            # synt.cha : PoS tags and full parses of Charniak.
      |---words                               # words : words.
    -data
      |---test-set.txt                        # get from .sh file.
      |---train-set.txt                       # get from .sh file.
    -temp
      |---GoogleNews-vectors-negative300.bin  # embedding file.
    -models
      |---model.pth                           # best model we get.
    -outputs
      |---outputs.txt                         # model outputs.
      |---test_outputs.txt                    # outputs that satisfies HW requirement.
    -make-testset.sh                          # run with bash to get test set.
    -make-trainset.sh                         # run with bash to get train set.
    -senmantic_role_labeler.txt               # log file.
    -srl-eval.pl
Command to run:
    python semantic_role_labeler.py
Description:
    Build and train a recurrent neural network (RNN) with hidden vector size 256.
    Loss function: Adam loss.
    Embedding vector: 300-dimensional.
    Learning rate: 0.0001.
    Batch size: 16