@inproceedings{guggilla-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {CGSRC} - Classifying Semantic Relations using Convolutional Neural Networks",
    author = "Guggilla, Chinnappa",
    editor = "Zock, Michael  and
      Lenci, Alessandro  and
      Evert, Stefan",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W16-5314/",
    pages = "104--109",
    abstract = "In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings achieved 43.15{\%} weighted-F1 accuracy on subtask-1 (checking existence of a relation between two terms) and 25.24{\%} weighted-F1 accuracy on subtask-2 (classifying relation types)."
}