標題: Titlebook: Computational Linguistics and Intelligent Text Processing; 18th International C Alexander Gelbukh Conference proceedings 2018 Springer Natu [打印本頁] 作者: Coagulant 時間: 2025-3-21 16:38
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)學科排名
書目名稱Computational Linguistics and Intelligent Text Processing網絡公開度
書目名稱Computational Linguistics and Intelligent Text Processing網絡公開度學科排名
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讀者反饋學科排名
作者: GIBE 時間: 2025-3-21 22:35 作者: 坦白 時間: 2025-3-22 02:43 作者: bioavailability 時間: 2025-3-22 04:45 作者: 時間等 時間: 2025-3-22 08:48 作者: fibula 時間: 2025-3-22 15:34 作者: fibula 時間: 2025-3-22 19:02 作者: MIR 時間: 2025-3-22 23:25 作者: conference 時間: 2025-3-23 03:32
,Grundlagen der Str?mungsmechanik,human–annotated affect values. Our framework outperforms the state–of–the–art generic and domain–specific approaches with a precision of over 70% for the emotion detection task on the SemEval 2007 Affect Corpus.作者: esculent 時間: 2025-3-23 07:30 作者: aneurysm 時間: 2025-3-23 11:24
,Grundgleichungen der Str?mungsmechanik,ensification, conditionality, tense, interrogation, and modality) within a large corpus of tweets with emotional hashtags. Our study sheds light on how to model negation relations between given emotions, reveals the impact of previously under-studied modifiers, and suggests how to detect more precise emotional statements.作者: Mingle 時間: 2025-3-23 14:40 作者: 過濾 時間: 2025-3-23 19:55 作者: creatine-kinase 時間: 2025-3-24 02:10 作者: white-matter 時間: 2025-3-24 05:31
BATframe: An Unsupervised Approach for Domain-Sensitive Affect Detectionhuman–annotated affect values. Our framework outperforms the state–of–the–art generic and domain–specific approaches with a precision of over 70% for the emotion detection task on the SemEval 2007 Affect Corpus.作者: V洗浴 時間: 2025-3-24 09:59 作者: affinity 時間: 2025-3-24 11:58 作者: 爵士樂 時間: 2025-3-24 17:25 作者: 圍裙 時間: 2025-3-24 21:00
Mining Aspect-Specific Opinions from Online Reviews Using a Latent Embedding Structured Topic Modelpinions from small or large numbers of reviews and to assign accurate sentiment to words. Experimental results for topic coherence, document sentiment classification, and a human evaluation all show that our proposed model achieves significant improvements over several state-of-the-art baselines.作者: GULLY 時間: 2025-3-25 00:22 作者: Semblance 時間: 2025-3-25 06:39
Sarcasm Annotation and Detection in Tweetse-of-the-art system for automatic sarcasm detection in tweets was implemented. Experiments on the two manually annotated datasets show comparable results, while deviating considerably from results on automatically annotated data, indicating that using hashtags is not a reliable approach to creating Twitter sarcasm corpora.作者: famine 時間: 2025-3-25 09:54 作者: ANT 時間: 2025-3-25 15:18
Benchmarking Multimodal Sentiment Analysist modalities, and generalizability. The framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.作者: 障礙物 時間: 2025-3-25 16:43 作者: Exaggerate 時間: 2025-3-25 23:49 作者: CARK 時間: 2025-3-26 00:26
Grundgleichungen der Str?mungsmechanikaffected. A formal model is specified that induces in a compositional, bottom-up manner informative relation tuples which indicate perspectives on attitudes. This enables the reader to focus on interesting cases, since they are directly accessible from the parts of the relation tuple.作者: 蒼白 時間: 2025-3-26 07:29 作者: Haphazard 時間: 2025-3-26 11:57
CSenticNet: A Concept-Level Resource for Sentiment Analysis in Chinese Languagepaper, we present a method for the construction of a Chinese sentiment resource. We utilize both English sentiment resources and the Chinese knowledge base NTU Multi-lingual Corpus. In particular, we first propose a resource based on SentiWordNet and a second version based on SenticNet.作者: languor 時間: 2025-3-26 15:22
Verb-Mediated Composition of Attitude Relations Comprising Reader and Writer Perspectiveaffected. A formal model is specified that induces in a compositional, bottom-up manner informative relation tuples which indicate perspectives on attitudes. This enables the reader to focus on interesting cases, since they are directly accessible from the parts of the relation tuple.作者: 我悲傷 時間: 2025-3-26 19:16
Customer Churn Prediction Using Sentiment Analysis and Text Classification of VOCptures a view of customer’s attitude and feedbacks. To the best of our knowledge, this is the first work that introduces text classification of VOC to churn prediction task. Experiments show that adding VOC analysis into a conventional churn prediction model results in a significant increase in predictive performance.作者: 施加 時間: 2025-3-26 23:53 作者: 嚴厲譴責 時間: 2025-3-27 02:03 作者: 滲入 時間: 2025-3-27 08:56
https://doi.org/10.1007/978-3-662-10107-0t modalities, and generalizability. The framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.作者: 施魔法 時間: 2025-3-27 11:30
Herbert Oertel,Martin B?hle,Ulrich Dohrmanngmented embedding and attention mechanism. The attention mechanism here is expected to locate the important parts of a text. The evaluation on SemEval 2016 Task 6 Twitter Stance Detection dataset shows that our proposed model achieves the state-of-the-art results.作者: GRE 時間: 2025-3-27 14:43
Herbert Oertel,Martin B?hle,Ulrich Dohrmannre provided as a benchmark for future studies and comparisons with other emotion detection models. The best results over a set of eight emotions were obtained using a complement Na?ve Bayes algorithm with an overall accuracy of 68.12%.作者: 易改變 時間: 2025-3-27 19:54
Leveraging Target-Oriented Information for Stance Classificationgmented embedding and attention mechanism. The attention mechanism here is expected to locate the important parts of a text. The evaluation on SemEval 2016 Task 6 Twitter Stance Detection dataset shows that our proposed model achieves the state-of-the-art results.作者: Autobiography 時間: 2025-3-27 23:00 作者: 移植 時間: 2025-3-28 05:34
https://doi.org/10.1007/978-3-319-77116-8artificial intelligence; emotion recognition; internet; learning algorithms; machine translations; natura作者: 事情 時間: 2025-3-28 07:13
978-3-319-77115-1Springer Nature Switzerland AG 2018作者: 健談的人 時間: 2025-3-28 11:10
Computational Linguistics and Intelligent Text Processing978-3-319-77116-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 享樂主義者 時間: 2025-3-28 17:08 作者: 最小 時間: 2025-3-28 19:02
Supervised?Domain?Adaptation via Label?Alignment for Opinion?Expression?Extractionjections that can improve the performance of a sequence model (e.g. CRF) in the target domain by align features with the true label sequence. We test our methods on product reviews and observe significant improvement in performance in comparison to baseline methods.作者: VAN 時間: 2025-3-29 00:11
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232599.jpg作者: myalgia 時間: 2025-3-29 06:59
https://doi.org/10.1007/978-3-642-88296-8such significant words as features from the corpus reduces the amount of irrelevant information in the feature set under supervised sentiment classification settings. In this paper, we conceptually study and compare various types of feature building methods, . . test and . for sentiment analysis tas作者: 柔美流暢 時間: 2025-3-29 09:50 作者: 天文臺 時間: 2025-3-29 12:22 作者: 防止 時間: 2025-3-29 18:26 作者: 異端 時間: 2025-3-29 20:37
Herbert Oertel,Martin B?hle,Ulrich Dohrmannxt, such as tweets (Twitter messages). The paper presents a comparison of three sets of tweets marked for sarcasm, two annotated manually and one annotated using the common strategy of relying on the authors correctly using hashtags to mark sarcasm. To evaluate the difficulty of the datasets, a stat作者: 全部 時間: 2025-3-30 00:56 作者: 擁擠前 時間: 2025-3-30 06:59 作者: 哪有黃油 時間: 2025-3-30 11:07 作者: Budget 時間: 2025-3-30 12:48
,Einführung in die Str?mungsmechanik,The inferred information focuses on the author’s attitude or opinion towards a written text. Although there is extensive research done on sentiment analysis on English language, there has been little work done that targets the morphologically rich structure of the Arabic language. In addition, most 作者: 實現 時間: 2025-3-30 19:57 作者: 合適 時間: 2025-3-31 00:24 作者: peak-flow 時間: 2025-3-31 03:57 作者: Monotonous 時間: 2025-3-31 07:13 作者: strdulate 時間: 2025-3-31 11:45
https://doi.org/10.1007/978-3-662-10107-0current study focuses on automatic speech emotion recognition based on classic and innovated machine learning approaches using simulated emotional speech data. Specifically, individual Gaussian mixture models (GMM) trained for each emotion, a universal background GMM model (UBM-GMM) adapted to each 作者: hysterectomy 時間: 2025-3-31 17:06
,Mischproze? als dynamisches System,anually extracting them is time-consuming. Several topic models have been proposed to simultaneously extract item aspects and user’s opinions on the aspects, as well as to detect sentiment associated with the opinions. However, existing models tend to find poor aspect-opinion associations when limit作者: SOB 時間: 2025-3-31 17:36
https://doi.org/10.1007/978-3-642-59486-1f an opinion. In order to produce a readable and comprehensible opinion summary, which is the main application of opinion target extraction, these occurrences are consolidated at the entity level in a second task. In this paper we argue that combining the two tasks, . extracting opinion targets usin作者: 鉤針織物 時間: 2025-4-1 00:38
Str?mungsmechanik nicht-newtonscher Fluidejections that can improve the performance of a sequence model (e.g. CRF) in the target domain by align features with the true label sequence. We test our methods on product reviews and observe significant improvement in performance in comparison to baseline methods.作者: 小卷發(fā) 時間: 2025-4-1 04:49
