Displaying 101 - 106 of 106
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Wittenburg, P., Brugman, H., Broeder, D., & Russel, A. (2004). XML-based language archiving. In Workshop Proceedings on XML-based Richly Annotaded Corpora (LREC2004) (pp. 63-69). Paris: ELRA - European Language Resources Association.
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Wittenburg, P., Gulrajani, G., Broeder, D., & Uneson, M. (2004). Cross-disciplinary integration of metadata descriptions. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 113-116). Paris: ELRA - European Language Resources Association. -
Wittenburg, P., Johnson, H., Buchhorn, M., Brugman, H., & Broeder, D. (2004). Architecture for distributed language resource management and archiving. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 361-364). Paris: ELRA - European Language Resources Association. -
Woensdregt, M., & Dingemanse, M. (2020). Other-initiated repair can facilitate the emergence of compositional language. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (
Eds. ), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 474-476). Nijmegen: The Evolution of Language Conferences. -
Yang, J., Van den Bosch, A., & Frank, S. L. (2020). Less is Better: A cognitively inspired unsupervised model for language segmentation. In M. Zock, E. Chersoni, A. Lenci, & E. Santus (
Eds. ), Proceedings of the Workshop on the Cognitive Aspects of the Lexicon ( 28th International Conference on Computational Linguistics) (pp. 33-45). Stroudsburg: Association for Computational Linguistics.Abstract
Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.Additional information
full text via ACL website -
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.
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