Semantic Specialization for Knowledge-based Word Sense Disambiguation

• This repository contains the trained model (projection heads) and sense/context embeddings used for training and evaluating the model.
• If you want to learn how to use these files, please refer to the semantic_specialization_for_wsd repository.

Trained Model (Projection Heads)

• File: checkpoints/baseline/last.ckpt
• This is one of the trained models used for reporting the main results (Table 2 in [Mizuki and Okazaki, EACL2023]).
  NOTE: Five runs were performed in total.
• The main hyperparameters used for training are as follows:

Sense/context embeddings

• Directory: data/bert_embeddings/
• Sense embeddings: bert-large-cased_WordNet_Gloss_Corpus.hdf5
• Context embeddings for the self-training objective: bert-large-cased_SemCor.hdf5
• Context embeddings for evaluating the WSD task: bert-large-cased_WSDEval-ALL.hdf5

Reference

@inproceedings{Mizuki:EACL2023,
    title     = "Semantic Specialization for Knowledge-based Word Sense Disambiguation",
    author    = "Mizuki, Sakae and Okazaki, Naoaki",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    series = {EACL},
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    pages = "3449--3462",
}

• arXiv version is also available.