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Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 13th China National Maosong Sun,Yang

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樓主: Lincoln
11#
發(fā)表于 2025-3-23 12:10:02 | 只看該作者
Distant Supervision for Relation Extraction via Sparse Representationclass are computed. Finally, we classify the test sample by assigning it to the object class that has minimal residual. Experimental results demonstrate that the noise term is effective to noise features and our approach significantly outperforms the state-of-the-art methods.
12#
發(fā)表于 2025-3-23 16:05:12 | 只看該作者
Learning the Distinctive Pattern Space Features for Relation Extractione maintaining pattern distinctiveness. To demonstrate the effectiveness of the proposed features, we conduct the experiments on a real world data set with 6 different relation types. Experimental results demonstrate that pattern space features significantly outperform State-of-the-art.
13#
發(fā)表于 2025-3-23 19:04:24 | 只看該作者
Query Expansion for Mining Translation Knowledge from Comparable Dataove the recall significantly and obtain candidates of sentence pairs with high quality. Thus, our methods can help to make good preparation for extracting both parallel sentences and fragments subsequently.
14#
發(fā)表于 2025-3-23 22:10:39 | 只看該作者
0302-9743 t International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2014, held in Wuhan, China, in October 2014. The 27 papers presented were carefully reviewed and selected from 233 submissions. The papers are organized in topical sections on word segmentation;
15#
發(fā)表于 2025-3-24 02:37:53 | 只看該作者
16#
發(fā)表于 2025-3-24 07:00:49 | 只看該作者
Juan Sastre,Federico V. Pallardo,Jose Vi?a comprehensively, we make a break away from the constraints of dependency trees, and extend to graphs. Moreover, we utilize SVM to parse semantic dependency graphs on the basis of parsing of dependency trees.
17#
發(fā)表于 2025-3-24 11:25:39 | 只看該作者
18#
發(fā)表于 2025-3-24 14:56:22 | 只看該作者
19#
發(fā)表于 2025-3-24 20:43:47 | 只看該作者
Reactions to Psychotropic Medication character-level features. In the conducted experiments, our model achieves an 88.6% word token f-score on the standard Brent version of the Bernstein-Ratner corpora. Moreover, on standard Chinese segmentation datasets, our method outperforms a baseline model by 1.9-2.9 f-score points.
20#
發(fā)表于 2025-3-25 02:45:35 | 只看該作者
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