@inproceedings{cabiddu-etal-2022-chisense,
    title = "{C}hi{S}ense-12: An {E}nglish Sense-Annotated Child-Directed Speech Corpus",
    author = "Cabiddu, Francesco  and
      Bott, Lewis  and
      Jones, Gary  and
      Gambi, Chiara",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.lrec-1.557/",
    pages = "5198--5205",
    abstract = "Language acquisition research has benefitted from the use of annotated corpora of child-directed speech to examine key questions about how children learn and process language in real-world contexts. However, a lack of sense-annotated corpora has limited investigations of child word sense disambiguation in naturalistic contexts. In this work, we sense-tagged 53 corpora of American and English speech directed to 958 target children up to 59 months of age, comprising a large-scale sample of 15,581 utterances for 12 ambiguous words. Importantly, we carefully selected target senses that we know - from previous investigations - young children understand. As such work was part of a project focused on investigating the role of verbs in child word sense disambiguation, we additionally coded for verb instances which took a target ambiguous word as verb object. We present experimental work where we leveraged our sense-tagged corpus ChiSense-12 to examine the role of verb-event structure in child word sense disambiguation, and we outline our plan to use Transformer-based computational architectures to test hypotheses on the role of different learning mechanisms underlying children word sense disambiguation performance."
}