@inproceedings{patel-bhattacharyya-2018-iterative,
    title = "An Iterative Approach for Unsupervised Most Frequent Sense Detection using {W}ord{N}et and Word Embeddings",
    author = "Patel, Kevin  and
      Bhattacharyya, Pushpak",
    editor = "Bond, Francis  and
      Vossen, Piek  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2018.gwc-1.34/",
    pages = "293--297",
    abstract = "Given a word, what is the most frequent sense in which it occurs in a given corpus? Most Frequent Sense (MFS) is a strong baseline for unsupervised word sense disambiguation. If we have large amounts of sense-annotated corpora, MFS can be trivially created. However, sense-annotated corpora are a rarity. In this paper, we propose a method which can compute MFS from raw corpora. Our approach iteratively exploits the semantic congruity among related words in corpus. Our method performs better compared to another similar work."
}