The 28th Language Lunch
Date: 2011-06-10
Location: G.07 Informatics Forum
Exploring Social Communication Skills in Children with ASD through Humour and Social Stories
Aurora,Constantin; Informatics; A.Constantin-2@sms.ed.ac.uk
Autism Spectrum Disorders (ASDs) is a term coined to cover a group of neurodevelopmental disorders associated with noticeable impairments in three domains: social interaction (inability to develop age-appropriate relationships), communication (difficulties with language) and imagination (“restricted repetitive and stereotyped patterns of behaviors, interests and activities” [1]). rnThere is no cure for ASDs, but there is strong evidence that early interventions can help children with ASD to become more independent and to acquire social and communication skills.rnExperts report that interventions using social stories in relation to social communication skills are effective for the treatment of children with ASD [2]. Research shows also that humour might be a very helpful tool in educational interventions for children with ASD [3].rnThis project aims to explore the social communication skills (specifically sharing attention, sharing emotion, reciprocity) in children with ASD by combining social stories and humour.rn
Hidden authorship in children
Thom,Scott-Phillips; PPLS; thom@ling.ed.ac.uk
Gerlind,Grosse; Max Planck Institute for Evolutionary Anthropology; gerlind.grosse@eva.mpg.de
Michael,Tomasello; Max Planck Institute for Evolutionary Anthropology; tomas@eva.mpg.de
Hidden authorship refers to those scenarios in which an informative act is produced, but in which the communicative intent behind it is hidden. Suppose, for example, that a dinner guest wishes for some more wine, but recognises that, for whatever reason, it would be somewhat impolite to ask for this directly.rnInstead, she places her empty glass in a conspicuous location where it is likely to be noticed by the host, but does not explicitly bring attention to the fact that the glass is empty. Hidden authorship is categorically different to other varieties of intentional communication; see the table, right.
A Scalable Probabilistic Classifier for Langugage Modeling
Joel,Lang; Informatics; J.Lang-3@sms.ed.ac.uk
We present a novel probabilistic classifier, which scales well to problems that involve a large number of classes and require training on large datasets. A prominent example of such a problem is language modeling. Our classifier is based on the assumption that each feature is associated with a predictive strength, which quantifies how well the feature can predict the class by itself. The predictions of individual features can then be combined according to their predictive strength, resulting in a model, whose parameters can be reliably and efficiently estimated. We show that a generative language model based on our classifier consistently matches modified Kneser-Ney smoothing and can outperform it if sufficiently rich features are incorporated.
Re-dating the loss of laryngeal air sacs in Homo sapiens
Richard,Littauer; LEL; richard.littauer@gmail.com
Laryngeal air sacs are a product of convergent evolution in many different species of primates, cervids, bats, and other mammals. In the case of Homo sapiens, their presence has been lost. This has been argued to have happened before Homo heidelbergensis, due to a loss of the bulla in the hyoid bone from Austrolopithecus afarensis (Martinez, 2008), at a range of 500kya to 3.3mya. (de Boer, to appear). Justifications for the loss of laryngeal air sacs include infection, the ability to modify breathing patterns and reduce need for an anti-hyperventilating device (Hewitt et al, 2002), and the selection against air sacs as they are disadvantageous for subtle, timed, and distinct sounds. (de Boer, to appear). Further, it has been suggested that the loss goes against the significant correlation of air sac retention to evolutionary growth in body mass (Hewitt et al., 2002). I argue that the loss of air sacs may have occurred more recently (less than 500kya), as the loss of the bulla in the hyoid does not exclude the possibility of airs sacs, as in cervids, where laryngeal air sacs can project between two muscles (Frey et al., 2007). Further, the weight measurements of living species as a justification for the loss of air sacs despite a gain in body mass I argue to be unfounded given archaeological evidence, which suggests that the laryngeal air sacs may have been lost only after size reduction in Homo sapiens from Homo heidelbergensis. Finally, I suggest two further justifications for loss of the laryngeal air sacs in homo sapiens. First, the linguistic niche of hunting in the environment in which early hominins hunters have been posited to exist – the savannah – would have been better suited to higher frequency, directional calls as opposed to lower frequency, multidirectional calls. The loss of air sacs would have then been directly advantageous, as lower frequencies produced by air sac vocalisations over bare ground have been shown to favour multidirectional over targeted utterances (Frey and Gebler, 2003). Secondly, the reuse of air stored in air sacs could have possibly been disadvantageous toward sustained, regular heavy breathing, as would occur again in a hunting environment.
Linear Algebra for Data Set Construction: NLP and Speech Synthesis Examples
Sarah,Luger; ILCC; s.k.k.luger@sms.ed.ac.uk
Karl B.,Isaac; CSTR; s0976649@sms.ed.ac.uk
Jonathan,Millin; ANC; J.J.Millin@sms.ed.ac.uk
Building data sets is problematic in Natural Language Processing (NLP) because it is costly and time consuming to develop them by hand. This motivates the use of existing data, but these data are often in the wrong form. Although widely used in other areas of computer science, linear algebra is rarely applied to the data creation aspect of NLP and speech synthesis problems. We present two examples of using matrix representations to facilitate the building of data sets from existing resources. The first demonstrates the smaller case of merging data sets for use in the evaluation of synthetic speech systems. The second, larger and more abstract example, uses a similar, but inverted, version of this method to gather training data from existing, web-based resources to build exams. This matrix-based approach increases access to, and the utility of, such data and better directs the development of good training sets.
