Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

Kim Anh Nguyen, nguyenkh@ims.uni-stuttgart.de

Code for paper Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network (EACL 2017).

Prerequisite

1. NetworkX: for creating the tree
2. spaCy: for parsing
3. Theano

Preprocessing

• Step 1: parse the corpus to create the patterns (preprocess/parse_corpus.py)
• Step 2: create the resources which is used to train the model (preprocess/create_resources.py)

Training models

python train_ant_syn_net.py -corpus <corpus_prefix> -data <dataset_prefix> -emb <embeddings_file> -model <model_name> -iter <iteration>

in which:

• <corpus_prefix>: the prefix of corpus
• <dataset_prefix>: the prefix of dataset
• <embeddings_file>: the embeddings file
• <model_name>: 1 for training the combined model or 0 for training the pattern-based model
• <iteration>: the number of epoch

Reference

@InProceedings{nguyen:2017:ant_syn_net
  author    = {Nguyen, Kim Anh and Schulte im Walde, Sabine and Vu, Ngoc Thang},
  title     = {Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
  year      = {2017},
  address = {Valencia, Spain},
}