Social image tags as a source of word embeddings: A Task-oriented Evaluation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    Distributional hypothesis has been playing a central role in statistical NLP. Recently, however, its limitation in incorporating perceptual and empirical knowledge is noted, eliciting a field of perceptually grounded computational semantics. Typical sources of features in such a research are image datasets, where images are accompanied by linguistic tags and/or descriptions. Mainstream approaches employ machine learning techniques to integrate/combine visual features with linguistic features. In contrast to or supplementing these approaches, this study assesses the effectiveness of social image tags in generating word embeddings, and argues that these generated representations exhibit somewhat different and favorable behaviors from corpus-originated representations. More specifically, we generated word embeddings by using image tags obtained from a large social image dataset YFCC100M, which collects Flickr images and the associated tags. We evaluated the efficacy of generated word embeddings with standard semantic similarity/relatedness tasks, which showed that comparable performances with corpus-originated word embeddings were attained. These results further suggest that the generated embeddings could be effective in discriminating synonyms and antonyms, which has been an issue in distributional hypothesis-based approaches. In summary, social image tags can be utilized as yet another source of visually enforced features, provided the amount of available tags is large enough.

    Original languageEnglish
    Title of host publicationLREC 2018 - 11th International Conference on Language Resources and Evaluation
    EditorsHitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga
    PublisherEuropean Language Resources Association (ELRA)
    Pages969-973
    Number of pages5
    ISBN (Electronic)9791095546009
    Publication statusPublished - 2019 Jan 1
    Event11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan
    Duration: 2018 May 72018 May 12

    Other

    Other11th International Conference on Language Resources and Evaluation, LREC 2018
    CountryJapan
    CityMiyazaki
    Period18/5/718/5/12

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    Keywords

    • Antonyms
    • Image tags
    • Semantic similarity
    • Social media
    • Synonyms
    • Word embeddings

    ASJC Scopus subject areas

    • Linguistics and Language
    • Education
    • Library and Information Sciences
    • Language and Linguistics

    Cite this

    Hasegawa, M., Kobayashi, T., & Hayashi, Y. (2019). Social image tags as a source of word embeddings: A Task-oriented Evaluation. In H. Isahara, B. Maegaard, S. Piperidis, C. Cieri, T. Declerck, K. Hasida, H. Mazo, K. Choukri, S. Goggi, J. Mariani, A. Moreno, N. Calzolari, J. Odijk, & T. Tokunaga (Eds.), LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 969-973). European Language Resources Association (ELRA).