Conversational success in Williams syndrome: communication in the face of cognitive and linguistic limitations

Williams syndrome (WS) is characterized by apparent relative strengths in language, facial processing and social cognition but by profound impairment in spatial cognition, planning and problem solving. Following recent research which suggests that individuals with WS may be less linguistically able than was once thought, in this paper we begin to investigate why and how they may give the impression of linguistic proficiency despite poor standardized test results. This case study of Brendan, a 12-year-old boy with WS, who presents with a considerable lack of linguistic ability, suggests that impressions of linguistic competence may to some extent be the result of conversational strategies which enable him to compensate for various cognitive and linguistic deficits with a considerable degree of success. These conversational strengths are not predicted by his standardized language test results, and provide compelling support for the use of approaches such as Conversation Analysis in the assessment of individuals with communication impairments.

Social interaction deficits and conversational inadequacy in Williams syndrome

Research on social communication skills in individuals with Williams syndrome has been inconclusive, with some arguing that these skills are a relative strength and others that they are a weakness. The aim of the present study was to investigate social interaction abilities in a group of children with WS, and to compare them to a group of children with specific language impairment and a group of typically developing children. Semi-structured conversations were conducted and 100-150 utterances were selected for analysis in terms of exchange structure, turn taking, information transfer and conversational inadequacy. The statistical analyses showed that the children with WS had difficulties with exchange structure and responding appropriately to the interlocutor's requests for information and clarification. They also had significant difficulties with interpreting meaning and providing enough information for the conversational partner. Despite similar language abilities with a group of children with specific language impairment, the children with WS had different social interaction skills, which suggests that they follow an atypical trajectory of development and their neurolinguistic profile does not directly support innate modularity. (c) 2005 Elsevier Ltd. All rights reserved.

Entity-based semantic search on conversational transcripts semantic

This paper describes the implementation of a semantic web search engine on conversation styled transcripts. Our choice of data is Hansard, a publicly available conversation style transcript of parliamentary debates. The current search engine implementation on Hansard is limited to running search queries based on keywords or phrases hence lacks the ability to make semantic inferences from user queries. By making use of knowledge such as the relationship between members of parliament, constituencies, terms of office, as well as topics of debates the search results can be improved in terms of both relevance and coverage. Our contribution is not algorithmic instead we describe how we exploit a collection of external data sources, ontologies, semantic web vocabularies and named entity extraction in the analysis of underlying semantics of user queries as well as the semantic enrichment of the search index thereby improving the quality of results.

Turing Test: Mindless Game?

The Turing Test, originally configured for a human to distinguish between an unseen man and unseen woman through a text-based conversational measure of gender, is the ultimate test for thinking. So conceived Alan Turing when he replaced the woman with a machine. His assertion, that once a machine deceived a human judge into believing that they were the human, then that machine should be attributed with intelligence. But is the Turing Test nothing more than a mindless game? We present results from recent Loebner Prizes, a platform for the Turing Test, and find that machines in the contest appear conversationally worse rather than better, from 2004 to 2006, showing a downward trend in highest scores awarded to them by human judges. Thus the machines are not thinking in the same way as a human intelligent entity would.