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Ordinarily, one thinks of the problem of natural language understanding as one of making a single, left-to-right pass through an input, producing a progressively refined and detailed interpretation. In text interpretation, however, the constraints of strict left-to-right processing are an encumbrance. Multi-pass methods , especially by interpreting words(More)
Recent work in text analysis has suggested that data on words that frequently occur together reveal important information about text content. Co-occurrence relations can serve two main purposes in language processing. First, the statistics of co-occurrence have been shown to produce accurate results in syntactic analysis. Second, the way that words appear(More)
This is the first and final collection of a noteworthy set of research papers, which is both historical and timely. PLNLP, the Programming Language for Natural Language Processing, which emerged at IBM's Thomas J. Watson Research Center in Yorktown Heights about 15 years ago, is ostensibly the glue that holds the volume's 22 chapters together. But it is as(More)
Neither natural language processing nor information retrieval is any longer a young field, but the two areas have yet to achieve a graceful interaction. Mainly, the reason for this incompatibility is that information retrieval technology depends upon relatively simple but robust methods, while natural language processing involves complex knowledge-based(More)
This paper presents an overview of the TIPSTER/SHOGUN project, the major results, and the SHOGUN data extraction system. TIP-STER/SHOGUN was a joint effort of Management and Data Systems (formerly GE Aerospace), part of the ARPA TIPSTER Text program. Two of the main technical thrusts of the project were: (1) the development of a model of finite-state(More)