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statistical_dependency_parsing's Introduction

Transition-based (Arc Eager) Dependency Parsing

Author: Touhidul Alam

Usage:

python3 parser.py [train/dev/test] [en/de] file_path_location

Example:

python3 parser.py train en /mount/studenten/dependency-parsing/data/english/train/wsj_train.only-projective.conll06 

Dependencies:

  • Numpy
  • pickle

Components

  • parser.py

    • State - class initialize initial stack, buffer, arcs, left-dependency and right-dependency
    • Instance - class saves the transition label with its corresponding features
    • Parser - class does oracle parsing during training and predicting and making a parser
  • reader.py

    • Corpus - class to read sentences from dataset and build them into sentence
    • Sentence - holds the datasets content
  • model.py

    • FeatureMapper - takes the unique feature and add it to the feature dictionary. Feature contains baseline feature, and extended features from Nivre (2011)
    • Model - saves the FeatureMapper and weights, implemented with Averaged perceptron

Output File

pred_en.conll06 - English
pred_de.conll06 - German

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