This was originally for a class project.

Utilizes a Chen and Manning (2014) style neural network parser in Python and TensorFlow. Many elements mimic SyntaxNet.

I analyze SyntaxNet's Architecture here.

parsing-config file is required to be created in the model directory before execution.

Run training_test.sh for an example of how to train a model. Evaluation during training works as well, but there is no API for tagging new input yet or serving a model.

External dependencies

• NumPy
• TensorFlow 1.0

Similarities to SyntaxNet

• Same embedding system (configurable per-feature group deep embedding)
• Same optimizer (Momentum with exponential moving average)
• Lexicon builder is identical for words, tags, and labels
• Map files output by SyntaxNet and AshParser should be identical
• Evaluation metric is identical (SyntaxNet's corresponds to AshParser's UAS)
• Feature system is almost identical (except perhaps some very rare corner cases)
• Due to same architecture, accuracy should be very close to Greedy SyntaxNet

Differences from SyntaxNet:

• Arc-Eager transition system also supported
• Context file with redundant or boilerplate information is unnecessary
• Supports GPU: training phase can complete in minutes
• Pure Python3 implementation. No need for bazel
• LAS (Labeled Attachment Score) prints out during evaluation
• Precalculation and caching of feature bags. This makes it easier to train multiple models with the same token features but different hyperparameters
• No support for structured (beam) parsing. Considering LSTM or something simpler and faster instead for the future. Accuracy loss should be in the ballpark of 1-2% due to this.
• Feature groups are automatically created by groups of tag, word, and label rather than by grouping together with semicolon in a context file
• Only support for the transition parser, not the POS tagger, morphological analyzer, or tokenizer
• ngrams, punctuation_amount, morph tags and other features not yet implemented