Sound-based distributional models

Alessandro Lopopolo, Emiel van Miltenburg

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

17 Citations (Scopus)

Abstract

Following earlier work in multimodal distributional semantics, we present the first results of our efforts to build a perceptually grounded semantic model. Rather than using images, our models are built on sound data collected from freesound.org. We compare three models: one bag-of-words model based on user-provided tags, a model based on audio features, using a ‘bag-of-audio-words’ approach and a model that combines the two. Our results show that the models are able to capture semantic relatedness, with the tag-based model scoring higher than the sound-based model and the combined model. However, capturing semantic relatedness is biased towards language-based models. Future work will focus on improving the sound-based model, finding ways to combine linguistic and acoustic information, and creating more reliable evaluation data.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Computational Semantics
Place of PublicationLondon
PublisherAssociation for Computational Linguistics
Pages70-75
Number of pages6
Publication statusPublished - 1 Apr 2015
Externally publishedYes

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  • Cite this

    Lopopolo, A., & van Miltenburg, E. (2015). Sound-based distributional models. In Proceedings of the 11th International Conference on Computational Semantics (pp. 70-75). Association for Computational Linguistics. http://www.aclweb.org/anthology/W15-0110