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Titlebook: Computational Linguistics and Intelligent Text Processing; 9th International Co Alexander Gelbukh Conference proceedings 2008 Springer-Verl

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樓主: LEVEE
11#
發(fā)表于 2025-3-23 10:05:28 | 只看該作者
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發(fā)表于 2025-3-23 17:22:19 | 只看該作者
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發(fā)表于 2025-3-24 01:33:06 | 只看該作者
Kontinuumsmechanische Grundlagen,tic information about both arguments and adjuncts of verbs. When combined with our best performing classifier (a novel Gaussian classifier), it yields the promising accuracy of 64.2% in classifying 204 verbs to 17 Levin (1993) classes. We discuss the impact of our results on the state-or-art and propose avenues for future work.
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發(fā)表于 2025-3-24 06:03:58 | 只看該作者
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發(fā)表于 2025-3-24 10:25:43 | 只看該作者
https://doi.org/10.1007/3-540-32487-9rd similarity. This work presents a linear time complexity approximative algorithm for computing word similarity without any dimensionality reduction. It then introduces a large-scale evaluation based on two languages and two knowledge sources and discusses the underlying reasons for the relative performance of each measure.
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發(fā)表于 2025-3-24 12:32:46 | 只看該作者
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發(fā)表于 2025-3-24 17:17:26 | 只看該作者
19#
發(fā)表于 2025-3-24 22:17:53 | 只看該作者
Acquisition of Elementary Synonym Relations from Biological Structured Terminologysing the terminological resource .. It provides results with over 93% precision. Comparison with an existing synonym resource (the general-language resource .) shows that there is a very small overlap between the induced lexicon of synonyms and the . synsets.
20#
發(fā)表于 2025-3-24 23:42:56 | 只看該作者
A Comparison of Co-occurrence and Similarity Measures as Simulations of Contextrd similarity. This work presents a linear time complexity approximative algorithm for computing word similarity without any dimensionality reduction. It then introduces a large-scale evaluation based on two languages and two knowledge sources and discusses the underlying reasons for the relative performance of each measure.
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