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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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發(fā)表于 2025-3-23 12:11:22 | 只看該作者
Boundary Layer Turbulence Behavior,nslated. Our multilingual translation system JANUS-2 is able to translate English and German spoken input into either English, German, Spanish, Japanese or Korean output. Getting optimal acoustic and language models as well as developing adequate dictionaries for all these languages requires a lot o
12#
發(fā)表于 2025-3-23 14:48:23 | 只看該作者
13#
發(fā)表于 2025-3-23 19:50:49 | 只看該作者
Alexander J. Smits,Jean-Paul Dussaugeignificantly improves performance. The bulk of the paper, however, attempts to answer the question: what did the program learn that would account for this improvement? We show that the program has learned many linguistically recognized forms of lexical information, particularly verb case frames and
14#
發(fā)表于 2025-3-24 01:08:37 | 只看該作者
Turbulent Shear Layers in Supersonic Flowoming an important issue in grammar building and parsing. The statistical induction of grammars and the statistical training of (hand written) grammars are ways to attain or improve a score, but a stochastic grammar does not reflect the often stereotypical use of words depending on their semantical
15#
發(fā)表于 2025-3-24 05:58:02 | 只看該作者
16#
發(fā)表于 2025-3-24 07:22:46 | 只看該作者
https://doi.org/10.1007/3-540-33591-9articularly difficult case. We describe a robust PP disambiguation procedure that learns from a text corpus. The method is based on a loglinear model, a type of statistical model that is able to account for combinations of multiple categorial features. A series of experiments that compare the loglin
17#
發(fā)表于 2025-3-24 14:29:39 | 只看該作者
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發(fā)表于 2025-3-24 16:28:29 | 只看該作者
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發(fā)表于 2025-3-24 20:35:05 | 只看該作者
20#
發(fā)表于 2025-3-25 01:07:02 | 只看該作者
Lecture Notes in Computer Sciencems typically require experts to hand-build dictionaries of extraction patterns for each new type of information to be extracted. This paper presents a system that can learn dictionaries of extraction patterns directly from user-provided examples of texts and events to be extracted from them. The sys
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