AntSynDistinction

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Antonym-Syntonym Distinction

Kim Anh Nguyen, [email protected]

Code for the paper Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction (ACL 2016). For more details please refer to Nguyen et al. (2016).

Lexical Contrast Information:

• Antonym files (adj, noun, verb): Each line contains one target word and its antonyms as follows (tab delimited):

  good bad evil

• Synonym files (adj, noun, verb): Each line contains one target word and its synonyms as follows (tab delimited):

  good practiced expert skillful in-force well estimable secure beneficial unspoilt dear honest...

• Context files (adj, noun, verb) that refer as W(c) in the Equation 3:

  • To create files of features, the system requires the spaCy tool.

  • Extract relations between target and contexts:

    python create_contexts.py -input <corpus_file> -output <output-file-name>

  • Create features (across adj, noun, verb):

    python create_features.py -input <contexts_file> -output <features_file>

• Corpus: a plain-text corpus is used to train word embeddings.

Configuration

See config.cfg to set agruments for model.

Running model

Command line:

java -jar dLCE.jar config.cfg vector-size window-size adj-boolean noun-boolean verb-boolean iteration

For example, training model with 300 dimensions; window-size = 5; lexical contrast of adj, noun, verb; and 3 iterations:

java -jar dLCE.jar config.cfg 300 5 True True True 3

Pre-trained embeddings

• Wikipedia corpus, 100dim, min-count=100: dLCE_100d_minFreq_100

Reference

@InProceedings{nguyen:2016:antsyn
  author    = {Nguyen, Kim Anh and Schulte im Walde, Sabine and Vu, Ngoc Thang},
  title     = {Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction},
  booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year      = {2016},
}