Publications

Displaying 201 - 203 of 203
  • Zhang, Y., & Yu, C. (2016). Examining referential uncertainty in naturalistic contexts from the child’s view: Evidence from an eye-tracking study with infants. In A. Papafragou, D. Grodner, D. Mirman, & J. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (CogSci 2016). Austin, TX: Cognitive Science Society (pp. 2027-2032). Austin, TX: Cognitive Science Society.

    Abstract

    Young Infants are prolific word learners even though they are facing the challenge of referential uncertainty (Quine, 1960). Many laboratory studies have shown that infants are skilled at inferring correct referents of words from ambiguous contexts (Swingley, 2009). However, little is known regarding how they visually attend to and select the target object among many other objects in view when parents name it during everyday interactions. By investigating the looking pattern of 12-month-old infants using naturalistic first-person images with varying degrees of referential ambiguity, we found that infants’ attention is selective and they only select a small subset of objects to attend to at each learning instance despite the complexity of the data in the real world. This work allows us to better understand how perceptual properties of objects in infants’ view influence their visual attention, which is also related to how they select candidate objects to build word-object mappings.
  • Zhang, Y., Amatuni, A., Crain, E., & Yu, C. (2020). Seeking meaning: Examining a cross-situational solution to learn action verbs using human simulation paradigm. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 2854-2860). Montreal, QB: Cognitive Science Society.

    Abstract

    To acquire the meaning of a verb, language learners not only need to find the correct mapping between a specific verb and an action or event in the world, but also infer the underlying relational meaning that the verb encodes. Most verb naming instances in naturalistic contexts are highly ambiguous as many possible actions can be embedded in the same scenario and many possible verbs can be used to describe those actions. To understand whether learners can find the correct verb meaning from referentially ambiguous learning situations, we conducted three experiments using the Human Simulation Paradigm with adult learners. Our results suggest that although finding the right verb meaning from one learning instance is hard, there is a statistical solution to this problem. When provided with multiple verb learning instances all referring to the same verb, learners are able to aggregate information across situations and gradually converge to the correct semantic space. Even in cases where they may not guess the exact target verb, they can still discover the right meaning by guessing a similar verb that is semantically close to the ground truth.
  • Zinn, C., Wittenburg, P., & Ringersma, J. (2010). An evolving eScience environment for research data in linguistics. In N. Calzolari, B. Maegaard, J. Mariani, J. Odjik, K. Choukri, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) (pp. 894-899). European Language Resources Association (ELRA).

    Abstract

    The amount of research data in the Humanities is increasing at fastspeed. Metadata helps describing and making accessible this data tointerested researchers within and across institutions. While metadatainteroperability is an issue that is being recognised and addressed,the systematic and user-driven provision of annotations and thelinking together of resources into new organisational layers havereceived much less attention. This paper gives an overview of ourevolving technological eScience environment to support suchfunctionality. It describes two tools, ADDIT and ViCoS, which enableresearchers, rather than archive managers, to organise and reorganiseresearch data to fit their particular needs. The two tools, which areembedded into our institute's existing software landscape, are aninitial step towards an eScience environment that gives our scientistseasy access to (multimodal) research data of their interest, andempowers them to structure, enrich, link together, and share such dataas they wish.

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