標題: Titlebook: Analysis of Images, Social Networks and Texts; 7th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Andrey V Conference proceedin [打印本頁] 作者: decoction 時間: 2025-3-21 17:56
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書目名稱Analysis of Images, Social Networks and Texts影響因子(影響力)學(xué)科排名
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書目名稱Analysis of Images, Social Networks and Texts網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Analysis of Images, Social Networks and Texts被引頻次
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書目名稱Analysis of Images, Social Networks and Texts讀者反饋學(xué)科排名
作者: 不易燃 時間: 2025-3-21 20:56 作者: intoxicate 時間: 2025-3-22 02:35
Co-authorship Network Embedding and Recommending Collaborators via Network Embeddingrs, such as friendship, common interests, and policy of university. We show that, having a temporal co-authorship network, it is possible to predict future publications. We solve the problem of recommending collaborators from the point of link prediction using graph embedding, obtained from co-autho作者: 專心 時間: 2025-3-22 06:37
Organizational Networks Revisited: Predictors of Headquarters-Subsidiary Relationship Perception, special attention is being paid to the effect of headquarters - subsidiary conflicts on the company performance, especially in relation to the subsidiaries’ resistance, both active and passive, to following the directives of the headquarters. A large number of theoretical approaches have been used作者: 內(nèi)閣 時間: 2025-3-22 11:17 作者: BULLY 時間: 2025-3-22 16:42 作者: 杠桿 時間: 2025-3-22 17:11
Extraction of Hypernyms from Dictionaries with a Little Help from Word Embeddingsom different dictionaries are clustered, then single words and multiwords are extracted as hypernym candidates. A classification-based approach on pre-trained word embeddings is implemented as a complementary technique. In total, we extracted about 40K unique hypernym candidates for 22K word entries作者: BILK 時間: 2025-3-23 01:05
Sentiment Analysis of Telephone Conversations Using Multimodal Datanditions of telephone conversations is not ideal and contains a lot of mistakes and inaccuracies arising at the stage of speech recognition. Today, there are almost no papers about the sentiment analysis of conversations using multimodal datasets for the Russian language. In this paper, we suggest t作者: 侵略者 時間: 2025-3-23 01:47 作者: LUMEN 時間: 2025-3-23 07:00
RusNLP: Semantic Search Engine for Russian NLP Conference Papers (Dialogue, AIST and AINL). The collected corpus spans across 12 years and contains about 400 academic papers in English. The presented web service allows searching for publications semantically similar to arbitrary user queries or to any given paper. Search results can be filtered by authors and th作者: 吼叫 時間: 2025-3-23 12:41
Russian Q&A Method Study: From Naive Bayes to Convolutional Neural Networksnal neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to 13 classes, expanding the dataset and improving the representativeness of s作者: escalate 時間: 2025-3-23 17:44
Extraction of Explicit Consumer Intentions from Social Network Messageswork users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows di作者: 衍生 時間: 2025-3-23 21:38 作者: 不整齊 時間: 2025-3-24 00:39 作者: 常到 時間: 2025-3-24 05:57 作者: 不知疲倦 時間: 2025-3-24 09:06
https://doi.org/10.1007/978-3-658-28741-2s, administrative support from the head office to subsidiaries, and levels of subsidiary integration. This is because social relationships between different actors inside the organization, the strength of ties and the size of networks, as well as other characteristics, could be the explanatory varia作者: BULLY 時間: 2025-3-24 13:24
https://doi.org/10.1007/978-3-658-16277-1nd we also build models for determining the sentiment intensity for individual modalities and a combination of them. Different classification algorithms are compared: linear, neural networks and ensembles of decision trees, where XGBoost works best for audio, Logistic Regression - for text and Light作者: justify 時間: 2025-3-24 16:22
Inequality and the Digital Economy% accuracy on the new dataset). We also tested several widely-used machine learning methods (logistic regression, Bernoulli Na?ve Bayes) trained on the new question representation. The best result of 72.38% accuracy (micro) was achieved with the CNN model. We also ran experiments on pertinent featur作者: 闖入 時間: 2025-3-24 22:25
https://doi.org/10.1007/978-3-319-78420-5es of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set inclu作者: etiquette 時間: 2025-3-25 02:32
Lorenzo Pupillo,Eli Noam,Leonard Waverman embeddings from the E-step. Second, we show that Biterm Topic Model?(Yan et al. [.]) and Word Network Topic Model?(Zuo et al. [.]) are equivalent with the only difference of tying word and context embeddings. We further extend these models by adjusting representation of each sliding window with a f作者: Custodian 時間: 2025-3-25 05:10 作者: 慷慨援助 時間: 2025-3-25 11:28
Organizational Networks Revisited: Predictors of Headquarters-Subsidiary Relationship Perceptions, administrative support from the head office to subsidiaries, and levels of subsidiary integration. This is because social relationships between different actors inside the organization, the strength of ties and the size of networks, as well as other characteristics, could be the explanatory varia作者: Pericarditis 時間: 2025-3-25 13:11 作者: 火光在搖曳 時間: 2025-3-25 18:58
Russian Q&A Method Study: From Naive Bayes to Convolutional Neural Networks% accuracy on the new dataset). We also tested several widely-used machine learning methods (logistic regression, Bernoulli Na?ve Bayes) trained on the new question representation. The best result of 72.38% accuracy (micro) was achieved with the CNN model. We also ran experiments on pertinent featur作者: 和藹 時間: 2025-3-25 20:56
Extraction of Explicit Consumer Intentions from Social Network Messageses of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set inclu作者: figure 時間: 2025-3-26 03:36
Probabilistic Approach for Embedding Arbitrary Features of Text embeddings from the E-step. Second, we show that Biterm Topic Model?(Yan et al. [.]) and Word Network Topic Model?(Zuo et al. [.]) are equivalent with the only difference of tying word and context embeddings. We further extend these models by adjusting representation of each sliding window with a f作者: 外形 時間: 2025-3-26 07:09
Learning Representations for Soft Skill Matchingoft skill masking and soft skill tagging..We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92% on the job dataset when fixing a precision to 95%.作者: 英寸 時間: 2025-3-26 09:03 作者: Lumbar-Spine 時間: 2025-3-26 16:01
H. T. MacGillivray,E. B. Thomsonpecifically, we show that audiences of media channels represented in the leading Russian social network VK, as well as their activities, significantly overlap. The audience of the oppositional TV channel is connected with the mainstream media through acceptable mediators such as a neutral business c作者: milligram 時間: 2025-3-26 19:16
https://doi.org/10.1007/978-3-658-28741-2rs, such as friendship, common interests, and policy of university. We show that, having a temporal co-authorship network, it is possible to predict future publications. We solve the problem of recommending collaborators from the point of link prediction using graph embedding, obtained from co-autho作者: 過于平凡 時間: 2025-3-26 22:17 作者: 名字 時間: 2025-3-27 01:58
Literature Review and Research Gap,ssions, the words combine to generate a different meaning. This is why, identification of non-compositional expressions (e.g. idioms) become important in natural language processing tasks such as machine translation and word sense disambiguation..In this study, we explored the performance of vector 作者: 潛伏期 時間: 2025-3-27 05:28 作者: 有毒 時間: 2025-3-27 10:04
https://doi.org/10.1007/978-3-658-16277-1om different dictionaries are clustered, then single words and multiwords are extracted as hypernym candidates. A classification-based approach on pre-trained word embeddings is implemented as a complementary technique. In total, we extracted about 40K unique hypernym candidates for 22K word entries作者: Gerontology 時間: 2025-3-27 16:12
https://doi.org/10.1007/978-3-658-16277-1nditions of telephone conversations is not ideal and contains a lot of mistakes and inaccuracies arising at the stage of speech recognition. Today, there are almost no papers about the sentiment analysis of conversations using multimodal datasets for the Russian language. In this paper, we suggest t作者: 提升 時間: 2025-3-27 21:46 作者: 過份好問 時間: 2025-3-27 22:08 作者: entail 時間: 2025-3-28 05:35
Inequality and the Digital Economynal neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to 13 classes, expanding the dataset and improving the representativeness of s作者: Harass 時間: 2025-3-28 08:13 作者: FLACK 時間: 2025-3-28 13:27 作者: aggravate 時間: 2025-3-28 16:10 作者: 責(zé)難 時間: 2025-3-28 18:54
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/156380.jpg作者: GUMP 時間: 2025-3-29 02:29 作者: Insufficient 時間: 2025-3-29 06:39
Joint Node-Edge Network Embedding for Link PredictionIn this paper, we consider new formulation of graph embedding algorithm, while learning node and edge representation under common constraints. We evaluate our approach on link prediction problem for co-authorship network of HSE researchers’ publications. We compare it with existing structural network embeddings and feature-engineering models.作者: 克制 時間: 2025-3-29 08:41
https://doi.org/10.1007/978-3-030-11027-7artificial intelligence; image processing; image segmentation; information retrieval; natural language p作者: 絕種 時間: 2025-3-29 13:00 作者: 數(shù)量 時間: 2025-3-29 19:23 作者: Canvas 時間: 2025-3-29 21:09
The Deep 2-Micron Survey of the Southern Skyn aggregates data about top researches and their fields of interest according to the Google Scholar service. The second graph is a map of largest on-line communities on data science on VKontakte platform.作者: gimmick 時間: 2025-3-30 01:11
Visualization of Data Science Community in Russian aggregates data about top researches and their fields of interest according to the Google Scholar service. The second graph is a map of largest on-line communities on data science on VKontakte platform.作者: arrhythmic 時間: 2025-3-30 06:55 作者: NAIVE 時間: 2025-3-30 08:27
Literature Review and Research Gap, samples of 1–3 sentences, which resembles the length of typical social media posts. We successfully evaluated our approach on the PAN?2014 challenge on authorship verification for English text. The presented system outperforms competing approaches in the PAN?2014 challenge when using 10 short text samples or more.作者: jumble 時間: 2025-3-30 12:38 作者: Delude 時間: 2025-3-30 16:50 作者: glucagon 時間: 2025-3-30 22:49 作者: MAG 時間: 2025-3-31 03:12 作者: 支柱 時間: 2025-3-31 05:53
