IDEAS home Printed from
   My bibliography  Save this article

Language comprehension as a multi‐label classification problem


  • Konstantin Sering
  • Petar Milin
  • R. Harald Baayen


The initial stage of language comprehension is a multilabel classification problem. Listeners or readers, presented with an utterance, need to discriminate between the intended words and the tens of thousands of other words they know. We propose to address this problem by pairing two networks. The first network is independently learned with the Rescorla Wagner model. The second network is based on the first network and learned with the rule of Widrow and Hoff. The first network has to recover from sublexical input features the meanings encoded in the language signal, resulting in a vector of activations over the lexicon. The second network takes this vector as input and further reduces uncertainty about the intended message. Classification performance for a lexicon with 52,000 entries is good. The model also correctly predicts several aspects of human language comprehension. By rejecting the traditional linguistic assumption that language is a (de)compositional system, and by instead espousing a discriminative approach, a more parsimonious yet highly effective functional characterization of the initial stage of language comprehension is obtained.

Suggested Citation

  • Konstantin Sering & Petar Milin & R. Harald Baayen, 2018. "Language comprehension as a multi‐label classification problem," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 339-353, August.
  • Handle: RePEc:bla:stanee:v:72:y:2018:i:3:p:339-353
    DOI: 10.1111/stan.12134

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Simon N. Wood & Zheyuan Li & Gavin Shaddick & Nicole H. Augustin, 2017. "Generalized Additive Models for Gigadata: Modeling the U.K. Black Smoke Network Daily Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1199-1210, July.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Yu-Ying Chuang & Elnaz Shafaei-Bajestan & R. Harald Baayen & James P. Blevins, 2019. "The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning," Complexity, Hindawi, vol. 2019, pages 1-39, January.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:stanee:v:72:y:2018:i:3:p:339-353. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.