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Titlebook: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing; Stefan Wermter,Ellen Riloff,Gabriele Schel

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51#
發(fā)表于 2025-3-30 09:15:18 | 只看該作者
Turbulent Particle-Laden Gas Flowseld, summarize the work that is presented here, and provide some additional references. In the final section we will highlight important general issues and trends based on the workshop discussions and book contributions.
52#
發(fā)表于 2025-3-30 15:35:19 | 只看該作者
53#
發(fā)表于 2025-3-30 17:43:01 | 只看該作者
54#
發(fā)表于 2025-3-31 00:23:06 | 只看該作者
Lecture Notes in Computer Sciencetem, called LIEP, learns patterns that recognize relationships between key constituents based on local syntax. Sets of patterns learned by LIEP for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns.
55#
發(fā)表于 2025-3-31 02:29:12 | 只看該作者
56#
發(fā)表于 2025-3-31 07:43:34 | 只看該作者
57#
發(fā)表于 2025-3-31 12:59:27 | 只看該作者
https://doi.org/10.1007/b137383al determiners from textually derived semantic representations, where the target category was removed from the input. Because texts are semantically underdetermined, these representations have some degree of noise. In generation we can still assign the correct category in many cases (83%). These res
58#
發(fā)表于 2025-3-31 17:25:53 | 只看該作者
59#
發(fā)表于 2025-3-31 19:57:05 | 只看該作者
60#
發(fā)表于 2025-3-31 21:45:32 | 只看該作者
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