Showcase: Japanese Predicate-Argument Structure (PAS) analyzer

Showcase is a Pytorch implementation of the Japanese Predicate-Argument Structure (PAS) analyser presented in the paper of Matsubayashi & Inui (2018) with some improvements. Given a input sentence, Showcase identifies verbal and nominal predicates in the sentence and detects their nominative (が), accusative (を), and dative (に) case arguments. The output case labels are based on the label definition of the NAIST Text Corpus where case markers in different voices are generalized into the case markers of an active voice.

Demo

http://www.cl.ecei.tohoku.ac.jp/showcase/

Usage

echo '今日は雨が降る' | showcase

cat example.txt | showcase

Input file format

• One raw sentence per line.
• A blank line can be used to segment a document. (Showcase just resets an argument index to zero.)

Requirements

• Python 3.5 (or higher)
  • We do not support Python 2
• CaboCha with JUMAN dict
• PyTorch 0.4.0

Instllation

Step 1. Install Showcase

pip install showcase-parser

Step 2: Download Resources

Resources include following files:

• 10 Model files for predicate detector (pred_model_0{0..9}.h5)
• 10 Model files for argument detector (arg_model_0{0..9}.h5)
• Word embedding Matrix (word_embedding.npz)
• POS embedding Matrix (pos_embedding.npz)
• Word index file (word.index)
• Part-of-Speech tag index file (pos.index)

Resources are all available at Google Drive.

• train/*.h5: models trained with the training set described in the paper.
• train-test/*.h5: models trained with the training and test sets.

Step 3: Create and edit config.json

Run showcase setup to create config.json file in $HOME/.config/showcase.

Then edit config.json and specify valid paths for:

• Resources downloaded in Step 2
• CaboCha and its JUMAN dictionary

Original config.json can be found at showcase/data/config.json of this repo.

You may specify path to config.json as follows:

showcase -c /path/to/config/config.json

Note that the apporopriate thresholds (hyperparameters) for arguments differ for each model. The thresholds for the provided models are described in the sample config file in each Google Drive directory.

(Re-)training

TBA

Step1: Train word2vec

TBA

Step2: Train model

TBA

Step3: Convert word2vec

• run get_vocab_from_word2vec.py and convert_word2vec_to_npy.py

Citation

@InProceedings{matsubayashi:2018:coling,
  author    = {Matsubayashi, Yuichiroh and Inui, Kentaro},
  title     = {Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate Argument Structure Analysis},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics (COLING)},
  year      = {2018},
}

Contributor

• Yuichiroh Matsubayashi
• Shun Kiyono