Abstract. We present an approach for automatic detection of topic change. Our approach is based on the analysis of statistical features of topics in time-sliced corpora and their dynamics over time. Processing large amounts of time-annotated news text, we identify new facets regarding a stream of topics consisting of latest news of public interest. Adaptable as an addition to the well known task of topic detection and tracking we aim to boil down a daily news stream to its novelty. For that we examine the contextual shift of the concepts over time slices. To quantify the amount of change, we adopt the volatility measure from econometrics and propose a new algorithm for frequency-independent detection of topic drift and change of meaning. The proposed measure does not rely on plain word frequency but the mixture of the co-occurrences of words. So, the analysis is highly independent of the absolute word frequencies and works over the whole frequency spectrum, especially also well for low-frequent words. Aggregating the computed time-related data of the terms allows to build overview illustrations of the most evolving terms for a whole time span.

We present an approach for knowledge-free and unsupervised recognition of compound nouns for languages that use one-wordcompounds such as Germanic and Scandinavian languages. Our approach works by creating a candidate list of compound splits based on the word list of a large corpus. Then, we filter this list using the following criteria: (a) frequencies of compounds and parts, (b) length of parts.In a second step, we search the corpus for periphrases, that is a reformulation of the (single-word) compound using the parts and very high frequency words (which are usually prepositions or determiners). This step excludes spurious candidate splits at cost of recall. To increase recall again, we train a trie-based classifier that also allows splitting multipart-compounds iteratively.We evaluate our method for both steps and with various parameter settings for German against a manually created gold standard, showing promising results above 80% precision for the splits and about half of the compounds periphrased correctly. Our method is language independent to a large extent, since we use neither knowledge about the language nor other language-dependent preprocessing tools.For compounding languages, this method can drastically alleviate the lexicon acquisition bottleneck, since even rare or yet unseen compounds can now be periphrased: the analysis then only needs to have the parts described in the lexicon, not the compound itself.

The amount of text data on the Internet is growing at a very fast rate. Online text repositories for news agencies, digital libraries and other organizations currently store gigaand tera-bytes of data. Large amounts of unstructured text poses a serious challenge for data mining and knowledge extraction. End user participation coupled with distributed computation can play a crucial role in meeting these challenges.In many applications involving classification of text documents, web users often participate in the tagging process. This collaborative tagging results in the formation of large scale Peer-to-Peer (P2P) systems which can function, scale and self-organize in the presence of highly transient population of nodes and do not need a central server for co-ordination. In this paper, we describe TagLearner, a P2P classifier learning system for extracting patterns from text data where the end users can participate both in the task of labeling the data and building a distributed classifier on it. We present a novel distributed linear programming based classification algorithm which is asynchronous in nature. The paper also provides extensive empirical results on text data obtained from an online repository -the NSF Abstracts Data.

Abstract. This paper is concerned with the evaluation of distributed and peer-to-peer information retrieval systems. A new measure is introduced that compares results of a distributed retrieval system to those of a centralised system, fully exploiting the ranking of the latter as an indicator of gradual relevance. Problems with existing evaluation approaches are verified experimentally.

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