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fellow, ieee Arithmetic coding provides an eeective mechanism for removing redundancy in the encoding of data. We show how arithmetic coding works and describe an eecient implementation that uses table lookup as a fast alternative to arithmetic operations. The reduced-precision arithmetic has a provably negligible eeect on the amount of compression achieved. We can speed up the implementation further by use of parallel processing. We discuss the role of probability models and how they provide probability information to the arithmetic coder. We conclude with perspectives on the comparative advantages and disadvantages of arithmetic coding.

This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or "real world" documents.This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words-indexing, clustering, retrieval, and summarization to name a few-could use the techniques described here to draw on a vast network of concepts and semantics.

http://www.cs.waikato.ac.nz/ml/weka.

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