There is an ongoing debate as to whether the words in early presyntactic forms of human language had simple atomic meanings like modern words, or whether they were holophrastic. Simulations were conducted using an iterated learning model in which the agents were able to associate words with meanings, but in which they were not able to use syntactic rules to combine words into phrases or sentences. In some of these simulations words emerged that had neither holophrastic nor atomic meanings, demonstrating the possibility of protolanguages intermediate between these two extremes. Further simulations show how increases in cognitive or articulatory capacity would have produced changes in the type of words that was dominant in protolanguages. It is likely that at some point in time humans spoke a protolanguage in which most words had neither holophrastic nor atomic meanings.

In order to determine the points at which meeting discourse changes from one topic to another, probabilistic models were used to approximate the process through which meeting transcripts were produced. Gibbs sampling was used to estimate the values of random variables in the models, including the locations of topic boundaries. This paper shows how discourse features were integrated into the Bayesian model and reports empirical evaluations of the benefit obtained through the inclusion of each feature and of the suitability of alternative models of the placement of topic boundaries. It demonstrates how multiple cues to segmentation can be combined in a principled way, and empirical tests show a clear improvement over previous work.

The effect of adding noise to an expression-induction model of language evolution was investigated. The model consisted of a number of artificial people who were able to infer the denotation of basic colour terms from examples of colours which the words had been used to identify, using a Bayesian inference procedure. The artificial people would express colours to one-another, so producing data from which other people could learn. Occasionally they would be creative, which allowed new words to enter the language. When certain points in the colour space were made especially salient, so that the artificial people were more likely to remember colours at these points, the languages emerging over a number of generations in evolutionary simulations replicated the typological patterns seen in the 110 languages of the world colour survey. It was found that if random noise was added to the data from which the artificial people learned, this had no major effect on the emergent languages, demonstrating that the Bayesian inference procedure is able to learn effectively despite the presence of random noise, even when placed in an evolutionary context

The Rich News system, that can automatically annotate radio and television news with the aid of resources retrieved from the World Wide Web, is described. Automatic speech recognition gives a temporally precise but conceptually inaccurate annotation model. Information extraction from related web news sites gives the opposite: conceptual accuracy but no temporal data. Our approach combines the two for temporally accurate conceptual semantic annotation of broadcast news. First low quality transcripts of the broadcasts are produced using speech recognition, and these are then automatically divided into sections corresponding to individual news stories. A key phrases extraction component finds key phrases for each story and uses these to search for web pages reporting the same event. The text and meta-data of the web pages is then used to create index documents for the stories in the original broadcasts, which are semantically annotated using the KIM knowledge management platform. A web interface then allows conceptual search and browsing of news stories, and playing of the parts of the media files corresponding to each news story. The use of material from the World Wide Web allows much higher quality textual descriptions and semantic annotations to be produced than would have been possible using the ASR transcript directly. The semantic annotations can form a part of the Semantic Web, and an evaluation shows that the system operates with high precision, and with a moderate level of recall.