Ahtract-We propose a novel, convenient fusion of nnturnl-language processing and fuzzy logic techniques for analyzing affect content in free text; our main goals are fast analysis and visualization of affect content for decision-making. The primary linguistic resource for fuzzy semantic typing is the fuzzy affect lcxicon, from which other important resources a r e generated, notably the fuzzy thesaurus and affect category groups. Free text is tagged with Rffect categories from the lexicon, and the affcct categories' centralities and intensities are combined using techniques from fuzzy logic to produce afCect setsfiizzy sets that represent thc affect quality of a document.We show diffcrcnt aspects of affect nnalysis using news stories and movie reviews. Our experiments show a very good carrespondencc of affect scts with human judgments of affect content. We ascribe this to the effective rcprcscntation of ambiguity in our fumy affect lexicon, and the ability of fuzzy logic to deal succcssfulty with thc ambiguity of words in natural language.Planricd extensions of the system include personalized profiles for Wcb-based content dissemination, fuzzy retrieval, clustcring and classification.
While complete understanding of arbitrary input text remains in the future, it is currently possible to construct natural language processing systems that provide a partial understanding of text with limited accuracy. Moreover, such systems can provide cost-effective solutions to commercially-significant business problems. This paper describes one such system: JASPER. JASPER is a fact extraction system recently developed and deployed by Carnegie Group for Reuters Ltd. JASPER uses a template-driven approach, partial understanding techniques, and heuristic procedures to extract certain key pieces of information from a limited range of text. We believe that many significant business problems can be solved by fact extraction applications which involve locating and extracting specific, predefined types of information from a limited range of text. The information extracted by such systems can be used in a variety of ways, such as filling in values in a database, generating summaries of the input text, serving as a part of the knowledge in an expert system, or feeding into another program which bases decisions on it. We expect to develop many such applications in the future using similar techniques.
This paper presents an analysis of a family of particular English constructions, all of which roughly express "purpose". In particular we look at the purpose clause, rationale .clause, and infinitival relative clause. We (1) show that couching the analysis in a computational framework, specifically generation, provides a more satisfying account than analyses based strictly on descriptive linguistics, (2) describe an implementation of our analysis in the natural language generation system MUMBLE-86, and (3) discuss how our architecture improves upon the techniques used by other generation systems for handling these and other adjunct constructions.
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