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Titlebook: Natural Language Processing Using Very Large Corpora; Susan Armstrong,Kenneth Church,David Yarowsky Book 1999 Springer Science+Business Me

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21#
發(fā)表于 2025-3-25 06:28:22 | 只看該作者
Implementation and Evaluation of a German HMM for POS Disambiguation,er re-estimation and full adaption of parameters from pre-tagged corpora. The ambiguity types resolved by this model are analysed and compared to ambiguity types of English and French. Finally, the model’s error types are described. I argue that although the overall performance of these models for G
22#
發(fā)表于 2025-3-25 07:28:40 | 只看該作者
Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging,gged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method for combining unsupervised and supervised rule-based training algorithms to create a highly accurate tagger using only a small amount of manually tagged te
23#
發(fā)表于 2025-3-25 15:02:14 | 只看該作者
24#
發(fā)表于 2025-3-25 19:11:49 | 只看該作者
Example-Based Sense Tagging of Running Chinese Text,, and outputs sense disambiguated text. Whereas previous work ((Yarowsky, 1992; Gale .., 1992; Gale .., 1993)) relies heavily on the role of statistics, the present system makes use of Machine Readable/Tractable Dictionaries ((Wilks .., 1990; Guo, 1995)) and an example-based reasoning technique ((Na
25#
發(fā)表于 2025-3-25 23:19:02 | 只看該作者
26#
發(fā)表于 2025-3-26 03:57:59 | 只看該作者
27#
發(fā)表于 2025-3-26 06:19:39 | 只看該作者
28#
發(fā)表于 2025-3-26 10:14:22 | 只看該作者
Statistical Augmentation of a Chinese Machine-Readable Dictionary,ly useful for extracting domain-specific and regional words not readily available in machine-readable dictionaries. Output was evaluated both using human evaluators and against a previously available dictionary. We also evaluated performance improvement in automatic Chinese tokenization. Results sho
29#
發(fā)表于 2025-3-26 15:45:46 | 只看該作者
30#
發(fā)表于 2025-3-26 20:10:39 | 只看該作者
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