標(biāo)題: Titlebook: Computational Linguistics and Intelligent Text Processing; 15th International C Alexander Gelbukh Conference proceedings 2014 Springer-Verl [打印本頁] 作者: vitamin-D 時(shí)間: 2025-3-21 20:01
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
作者: 凈禮 時(shí)間: 2025-3-21 21:14
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作者: –scent 時(shí)間: 2025-3-22 00:54 作者: Biguanides 時(shí)間: 2025-3-22 08:05 作者: Intellectual 時(shí)間: 2025-3-22 09:51 作者: 努力趕上 時(shí)間: 2025-3-22 15:44
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.作者: 努力趕上 時(shí)間: 2025-3-22 18:45
Computational Linguistics and Intelligent Text Processing15th International C作者: ellagic-acid 時(shí)間: 2025-3-22 21:14 作者: 遺棄 時(shí)間: 2025-3-23 02:45
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.作者: 被告 時(shí)間: 2025-3-23 05:44
,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作者: 致敬 時(shí)間: 2025-3-23 13:23
Conclusion: From , to Trump, and Beyondasked items were found more efficiently than bestseller recommender system and showed that items only at the Japanese site that seemed to be related to buyers’ interests could be found by the system in more realistic scenario.作者: 清楚說話 時(shí)間: 2025-3-23 17:11 作者: NAVEN 時(shí)間: 2025-3-23 20:45 作者: 裁決 時(shí)間: 2025-3-24 00:09
https://doi.org/10.1007/978-3-031-62975-4ocial 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.作者: 大罵 時(shí)間: 2025-3-24 03:46
https://doi.org/10.1007/978-3-663-04931-9d on Gaussian distribution which can analyze an analysis of semantic fuzziness of Chinese sentiment words quantitatively. Furthermore, several equations are proposed to calculate the polarities and strengths of sentiment words. Experimental results show that our method is highly effective.作者: LAP 時(shí)間: 2025-3-24 07:33
,Modelluntersuchungen zur Strahllüftung,essional exchanges. In this paper, we focus on the emotions expressed by the authors of the messages and more precisely on the targets of these emotions. We suggest an innovative method to identify these targets, based on the notion of semantic roles and using the FrameNet resource. Our method has been successfully validated on real data set.作者: 行為 時(shí)間: 2025-3-24 11:52
https://doi.org/10.1007/978-3-031-62975-4 for it. The empirical studies on Chinese-to-English translation tasks show that, even in comparison with a competitive baseline which employs well designed cube pruning, our approaches still double the decoding speed without compromising translation quality. The approaches have already been applied to an online commercial translation system.作者: adhesive 時(shí)間: 2025-3-24 17:44
,Modelluntersuchungen zur Strahllüftung,f Sina Social News with user emotion votes show that the proposed approach which do not use any news content, achieves a comparable performance to Bag-Of-Word model using both the headlines and the news contents, making our method more efficient in reader emotion prediction.作者: gait-cycle 時(shí)間: 2025-3-24 20:30
,Modelluntersuchungen zur Strahllüftung,e (SVM) algorithm can be reached. Over 75% cross-validation accuracy is reached when classifying the author gender of blog texts. Our findings show positive implications of emotion-based features on assisting author’s gender classification.作者: 桶去微染 時(shí)間: 2025-3-25 01:21
Conjunctural Analysis Part Two: The Case of ve opinions, while all hotel aspects and amenities are marked. In this paper, we present the design and a first study of the corpus. We reveal patterns of local sentiment that correlate with sentiment scores, thereby defining a promising starting point for an effective argumentation analysis.作者: BRINK 時(shí)間: 2025-3-25 03:47 作者: Ibd810 時(shí)間: 2025-3-25 09:15
Reader Emotion Prediction Using Concept and Concept Sequence Features in News Headlinesf Sina Social News with user emotion votes show that the proposed approach which do not use any news content, achieves a comparable performance to Bag-Of-Word model using both the headlines and the news contents, making our method more efficient in reader emotion prediction.作者: 玉米 時(shí)間: 2025-3-25 13:15
Investigating the Role of Emotion-Based Features in Author Gender Classification of Texte (SVM) algorithm can be reached. Over 75% cross-validation accuracy is reached when classifying the author gender of blog texts. Our findings show positive implications of emotion-based features on assisting author’s gender classification.作者: Junction 時(shí)間: 2025-3-25 17:29 作者: IRS 時(shí)間: 2025-3-25 20:14 作者: 勛章 時(shí)間: 2025-3-26 03:58 作者: 占線 時(shí)間: 2025-3-26 07:18 作者: interior 時(shí)間: 2025-3-26 10:51
https://doi.org/10.1007/978-3-663-04930-2 is a better choice. We identify context using a game with a purpose that increases the workers’ engagement in this complex task. With the contextual knowledge we obtain from only a small set of answers, we already halve the sentiment lexicons’ performance gap relative to human performance.作者: inchoate 時(shí)間: 2025-3-26 14:43
,Modelluntersuchungen zur Strahllüftung, sentiment classification. In addition we demonstrate that through use of the Multinomial Na?ve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.作者: Flavouring 時(shí)間: 2025-3-26 19:07 作者: Emmenagogue 時(shí)間: 2025-3-26 23:43
Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose is a better choice. We identify context using a game with a purpose that increases the workers’ engagement in this complex task. With the contextual knowledge we obtain from only a small set of answers, we already halve the sentiment lexicons’ performance gap relative to human performance.作者: 遷移 時(shí)間: 2025-3-27 02:15 作者: 侵害 時(shí)間: 2025-3-27 05:34 作者: scrape 時(shí)間: 2025-3-27 12:42
Conference proceedings 2014telligent Text Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The 85 revised papers presented together with 4 invited papers were carefully reviewed and selected from 300 submissions. The papers are organized in the following topical sections: lexica作者: 充足 時(shí)間: 2025-3-27 16:52 作者: DAUNT 時(shí)間: 2025-3-27 19:58 作者: 制造 時(shí)間: 2025-3-28 01:40 作者: 打火石 時(shí)間: 2025-3-28 02:52 作者: 收集 時(shí)間: 2025-3-28 06:46
A Method of Polarity Computation of Chinese Sentiment Words Based on Gaussian Distributiond on Gaussian distribution which can analyze an analysis of semantic fuzziness of Chinese sentiment words quantitatively. Furthermore, several equations are proposed to calculate the polarities and strengths of sentiment words. Experimental results show that our method is highly effective.作者: Optic-Disk 時(shí)間: 2025-3-28 11:22
Identifying the Targets of the Emotions Expressed in Health Forumsessional exchanges. In this paper, we focus on the emotions expressed by the authors of the messages and more precisely on the targets of these emotions. We suggest an innovative method to identify these targets, based on the notion of semantic roles and using the FrameNet resource. Our method has been successfully validated on real data set.作者: FELON 時(shí)間: 2025-3-28 15:14
Beam-Width Adaptation for Hierarchical Phrase-Based Translation for it. The empirical studies on Chinese-to-English translation tasks show that, even in comparison with a competitive baseline which employs well designed cube pruning, our approaches still double the decoding speed without compromising translation quality. The approaches have already been applied to an online commercial translation system.作者: 脫毛 時(shí)間: 2025-3-28 22:06
https://doi.org/10.1007/978-3-642-54903-8clustering and classification; document management and text processing; information retrieval; informat作者: 精確 時(shí)間: 2025-3-29 00:57 作者: 開頭 時(shí)間: 2025-3-29 07:01 作者: HEW 時(shí)間: 2025-3-29 07:34
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232602.jpg作者: nuclear-tests 時(shí)間: 2025-3-29 13:46
F. Schultz-Grunow,A. Jogwich,P. Sandsitive, negative or neutral. Modality is commonly used in text. In a typical corpus, there are around 18% of sentences with modality. Due to modality’s special characteristics, the sentiment it bears may be hard to determine. For example, in the sentence, ., the speaker is negative about this phone 作者: 閹割 時(shí)間: 2025-3-29 19:31
https://doi.org/10.1007/978-3-663-04930-2ents. Experiments were carried out on the 2012 Audio/Visual Emotion Challenge (AVEC2012) database, where emotions are defined as vectors in the Arousal-Expectancy-Power-Valence emotional space. Our model using 6 novel disfluency features yields significant improvements compared to those using large 作者: 自作多情 時(shí)間: 2025-3-29 23:19 作者: 神刊 時(shí)間: 2025-3-30 01:26 作者: Dorsal 時(shí)間: 2025-3-30 04:29 作者: 繼而發(fā)生 時(shí)間: 2025-3-30 08:56 作者: 男生戴手銬 時(shí)間: 2025-3-30 15:16 作者: grandiose 時(shí)間: 2025-3-30 19:09
,Modelluntersuchungen zur Strahllüftung, of exchange where patients, on condition of anonymity, can talk about their personal experiences freely. These resources are a gold mine for health professionals, giving them access to patient to patient exchanges, patient to health professional exchanges and even health professional to health prof作者: 不可接觸 時(shí)間: 2025-3-30 21:36 作者: CORD 時(shí)間: 2025-3-31 03:21 作者: Latency 時(shí)間: 2025-3-31 07:45 作者: Volatile-Oils 時(shí)間: 2025-3-31 10:52 作者: Adherent 時(shí)間: 2025-3-31 15:46
Conclusion: From , to Trump, and Beyonds. With the aim of minimizing the human input required we produced a manually normalized lexicon of the most salient out-of-vocabulary (OOV) tokens and used it to train a character-level statistical machine translation system (CSMT). Best results were obtained by combining the manually constructed l作者: 鳥籠 時(shí)間: 2025-3-31 20:58
Conclusion: From , to Trump, and Beyondl influences of other individuals. All of these factors can be identified in the language that individuals use to discuss their work with their peers. Previous approaches to modeling motivation have relied on social-network and time-series analysis to predict the popularity of a contribution to user作者: 棲息地 時(shí)間: 2025-4-1 00:17 作者: Frequency-Range 時(shí)間: 2025-4-1 04:07
