Abstract. Peer-to-peer (P 2 P) networking continuously gains popularity among computing science researchers. The problem of information retrieval (IR) over P 2 P networks is being addressed by researchers attempting to provide valuable insight as well as solutions for its successful deployment. All published studies have, so far, been evaluated by simulation means, using well-known document collections (usually acquired from TREC). Researchers test their systems using divided collections whose documents have been previously distributed to a number of simulated peers. This practice leads to two problems: First, there is little justification in favour of the document distributions used by relevant studies and second, since different studies use different experimental testbeds, there is no common ground for comparing the solutions proposed. In this work, we contribute a number of different document testbeds for evaluating P 2 P IR systems. Each of these has been deduced from TREC's WT10g collection and corresponds to different potential P 2 P IR application scenarios. We analyse each methodology and testbed with respect to the document distributions achieved as well as to the location of relevant items within each setting. This work marks the beginning of an effort to provide more realistic evaluation environments for P 2 P IR systems as well as to create a common ground for comparisons of existing and future architectures.

The lexicalist approach to Machine Translation offers significant advantages in the development of linguistic descriptions. However, the Shake-and-Bake generation algorithm of (Whitelock, 1992) is NPcomplete. We present a polynomial time algorithm for lexicalist MT generation provided that sufficient information can be transferred to ensure more determinism.

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We present the design of a practical context-sensitive glosser, incorporating current techniques for lightweight linguistic analysis based on large-scale lexical resources. We outline a general model for ranking the possible translations of the words and expressions that make up a text. This information can be used by a simple resource-bounded algorithm, of complexity O(n log n) in sentence length, that determines a consistent gloss of best translations. We then describe how the results of the general ranking model may be approximated using a simple heuristic prioritisation scheme. Finally we present a preliminary evaluation of the glosser's performance.