Titlebook: Computational Linguistics and Intelligent Text Processing; 15th International C Alexander Gelbukh Conference proceedings 2014 Springer-Verl

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書目名稱Computational Linguistics and Intelligent Text Processing影響因子(影響力)




書目名稱Computational Linguistics and Intelligent Text Processing影響因子(影響力)學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing網(wǎng)絡(luò)公開度




書目名稱Computational Linguistics and Intelligent Text Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing被引頻次




書目名稱Computational Linguistics and Intelligent Text Processing被引頻次學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing年度引用




書目名稱Computational Linguistics and Intelligent Text Processing年度引用學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing讀者反饋




書目名稱Computational Linguistics and Intelligent Text Processing讀者反饋學(xué)科排名

凈禮

A Sentence Vector Based Over-Sampling Method for Imbalanced Emotion Classificationd training dataset. Evaluations on NLP&CC2013 Chinese micro blog emotion classification dataset shows that the obtained classifier achieves 48.4% average precision, an 11.9 percent improvement over the state-of-art performance on this dataset (at 36.5%). This result shows that the proposed over-samp

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Biguanides

Intellectual

努力趕上

Extracting Social Events Based on Timeline and User Reliability Analysis on Twitterocial issues are selected and experimented on Korean twitter test set. The experimental results showed 97.2% in precision for the top 10 extracted events (P@10) on each day. This result shows that the proposed method is effective for extracting events in twitter corpus.

努力趕上

Computational Linguistics and Intelligent Text Processing15th International C

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遺棄

https://doi.org/10.1007/978-3-663-04930-2orrelation-based Feature-subset Selection. Considering overall performance on all emotion dimensions, our bimodal model outperforms the second best model of the challenge, and comes close to the best model. It also gives the best result when predicting Expectancy values.

被告

,Modelluntersuchungen zur Strahllüftung,d training dataset. Evaluations on NLP&CC2013 Chinese micro blog emotion classification dataset shows that the obtained classifier achieves 48.4% average precision, an 11.9 percent improvement over the state-of-art performance on this dataset (at 36.5%). This result shows that the proposed over-samp