Applying A Normalized Compression Metric To The Measurement Of Dialect Distance

The paper discusses the application of a similarity metric based on compression to the measurement of the distance among Bulgarian dia- lects. The similarity metric is de ned on the basis of the notion of Kolmo- gorov complexity of a le (or binary string). The application of Kolmogorov complexity in practice is not possible because its calculation over a le is an undecidable problem. Thus, the actual similarity metric is based on a real life compressor which only approximates the Kolmogorov complexity. To use the metric for distance measurement of Bulgarian dialects we rst represent the dialectological data in such a way that the metric is applicable. We propose two such representations which are compared to a baseline distance between dialects. Then we conclude the paper with an outline of our future work.

The Computational Analysis of Bulgarian Dialect Pronunciation

The paper presents a computational analysis of Bulgarian dialect variation, concentrating on pronunciation differences. It describes the phonetic data set compiled during the project* ‘Measuring Linguistic Unity and Diversity in Europe’ that consists of the pronunciations of 157 words collected at 197 sites from all over Bulgaria. We also present the results of analyzing this data set using various quantitative methods and compare them to the traditional scholarship on Bulgarian dialects. The results have shown that various dialectometrical techniques clearly identify east-west division of the country along the ‘jat’ border, as well as the third group of varieties in the Rodopi area. The rest of the groups specified in the traditional atlases either were not confirmed or were confirmed with a low confidence.

Defining Network Activity Patterns Using First Order Temporal Logics

Part of network management is collecting information about the activities that go on around a distributed system and analyzing it in real time, at a deferred moment, or both. The reason such information may be stored in log files and analyzed later is to data-mine it so that interesting, unusual, or abnormal patterns can be discovered. In this paper we propose defining patterns in network activity logs using a dialect of First Order Temporal Logics (FOTL), called First Order Temporal Logic with Duration Constrains (FOTLDC). This logic is powerful enough to describe most network activity patterns because it can handle both causal and temporal correlations. Existing results for data-mining patterns with similar structure give us the confidence that discovering DFOTL patterns in network activity logs can be done efficiently.