16,867 research outputs found
Does Language Determine Our Scientific Ideas?
SummaryThis paper argues that the influence of language on science, philosophy and other field is mediated by communicative practices. Where communications is more restrictive, established linguistic structures exercise a tighter control over innovations and scientifically motivated reforms of language. The viewpoint here centers on the thesis that argumentation is crucial in the understanding and evaluation of proposed reforms and that social practices which limit argumentation serve to erode scientific objectivity. Thus, a plea is made for a sociology of scientific belief designed to understand and insure social‐institutional conditions of the possibility of knowledge and its growth. A chief argument draws on work of Axelrod concerning the evolution of cooperation
Modeling Belief in Dynamic Systems, Part II: Revision and Update
The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines temporal and epistemic modalities with a notion of plausibility,
allowing us to examine the change of beliefs over time. In this paper, we show
how belief revision and belief update can be captured in our framework. This
allows us to compare the assumptions made by each method, and to better
understand the principles underlying them. In particular, it shows that Katsuno
and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on
several strong assumptions that may limit its applicability in artificial
intelligence. Finally, our analysis allow us to identify a notion of minimal
change that underlies a broad range of belief change operations including
revision and update.Comment: See http://www.jair.org/ for other files accompanying this articl
Extraction of Keyphrases from Text: Evaluation of Four Algorithms
This report presents an empirical evaluation of four algorithms for automatically extracting keywords and keyphrases from documents. The four algorithms are compared using five different collections of documents. For each document, we have a target set of keyphrases, which were generated by hand. The target keyphrases were generated for human readers; they were not tailored for any of the four keyphrase extraction algorithms. Each of the algorithms was evaluated by the degree to which the algorithms keyphrases matched the manually generated keyphrases. The four algorithms were (1) the AutoSummarize feature in Microsofts Word 97, (2) an algorithm based on Eric Brills part-of-speech tagger, (3) the Summarize feature in Veritys Search 97, and (4) NRCs Extractor algorithm. For all five document collections, NRCs Extractor yields the best match with the manually generated keyphrases
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