Evaluating Word Embeddings for Language Acquisition

Raquel Garrido Alhama, Caroline Rowland, Evan Kidd

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

    Abstract

    Continuous vector word representations (or word embeddings) have shown success in capturing semantic relations between words, as evidenced with evaluation against behavioral data of adult performance on semantic tasks (Pereira et al. 2016). Adult semantic knowledge is the endpoint of a language acquisition process; thus, a relevant question is whether these models can also capture emerging word representations of young language learners. However, the data of semantic knowledge of children is scarce or non-existent for some age groups. In this paper, we propose to bridge this gap by using Age of Acquisition norms to evaluate word embeddings learnt from child-directed input. We present two methods that evaluate word embeddings in terms of (a) the semantic neighbourhood density of learnt words, and (b) the convergence to adult word associations. We apply our methods to bag-of-words models, and we find that (1) children acquire words with fewer semantic neighbours earlier, and (2) young learners only attend to very local context. These findings provide converging evidence for validity of our methods in understanding the prerequisite features for a distributional model of word learning.
    Original languageEnglish
    Title of host publicationProceedings of the Workshop on Cognitive Modeling and Computational Linguistics
    PublisherAssociation for Computational Linguistics
    Pages38-42
    Number of pages5
    DOIs
    Publication statusPublished - Nov 2020
    Event2020 Conference on Empirical Methods in Natural Language Processing - Online
    Duration: 16 Nov 202020 Nov 2020
    https://2020.emnlp.org/

    Conference

    Conference2020 Conference on Empirical Methods in Natural Language Processing
    Abbreviated titleEMNLP 2020
    Period16/11/2020/11/20
    Internet address

    Keywords

    • Word Embeddings
    • Language Acquisition
    • Semantic Relations

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