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Titlebook: Edmund Husserl’s Theory of Meaning; J. N. Mohanty Book 1976 Martinus Nijhoff, The Hague, Netherlands 1976 Edmund Husserl.concept.edition.e

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31#
發(fā)表于 2025-3-27 00:12:10 | 只看該作者
Expression and Its Functions,ing, or giving expression to a meaning. In other words, not all signs are expressions. The concept of sign is wider than the concept of expression, for expressions are . signs inasmuch as the functions of signifying and meaning are interlaced in them. This does not imply, Husserl warns us, that the
32#
發(fā)表于 2025-3-27 04:34:24 | 只看該作者
Thingking and Meaning,l fault of his argument does not lie in the fact that he exaggerates the definiteness of our imagery. Warnock has rightly pointed out that Berkeley survives such criticism. The real fault lies in Berkeley’s imagist notion of thinking. Locke’s abstract idea was understood as an abstract image, and it
33#
發(fā)表于 2025-3-27 06:38:11 | 只看該作者
34#
發(fā)表于 2025-3-27 11:33:42 | 只看該作者
35#
發(fā)表于 2025-3-27 17:22:49 | 只看該作者
Formal Logic,dy the exact nature of this discipline along with its inner stratifications and outer ramifications. In connection with formal logic Husserl was chiefly preoccupied with these two problems. What he has given us therefore in his . and the . is not a system of logic but a systematic .-logic.
36#
發(fā)表于 2025-3-27 21:20:15 | 只看該作者
Back to Experience,ithin formal logic, therefore, an appeal to . (in the sense to be specified below) is not called for. [In the construction of a formal system, the only sense in which an appeal to intuition . be permitted would be through the process of construction: it is in this sense that the intuitionists oppose
37#
發(fā)表于 2025-3-28 00:57:47 | 只看該作者
38#
發(fā)表于 2025-3-28 02:26:24 | 只看該作者
39#
發(fā)表于 2025-3-28 09:23:01 | 只看該作者
40#
發(fā)表于 2025-3-28 13:09:51 | 只看該作者
Centrality Speeds the Subgraph Isomorphism Search Up in Target Aware?Contexts Results on benchmarks of biological networks show the competitiveness of the proposed seven alternatives and that, among them, local strategies predominate on sparse target graphs, and closeness- and eigenvector-based strategies outperform on dense graphs.
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