標(biāo)題: Titlebook: Analysis of Images, Social Networks and Texts; 9th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu Conference proceedin [打印本頁(yè)] 作者: 不能平庸 時(shí)間: 2025-3-21 18:39
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書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts被引頻次
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書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts讀者反饋
書(shū)目名稱(chēng)Analysis of Images, Social Networks and Texts讀者反饋學(xué)科排名
作者: constitute 時(shí)間: 2025-3-22 00:00 作者: 蚊帳 時(shí)間: 2025-3-22 03:45 作者: 過(guò)分自信 時(shí)間: 2025-3-22 06:32
https://doi.org/10.1007/978-3-8348-9038-2veral concerns given the critical impact that biased decisions may have on individuals or on society as a whole. Not only unfair outcomes affect human rights, they also undermine public trust in ML and AI. In this paper we address fairness issues of ML models based on decision outcomes, and we show 作者: staging 時(shí)間: 2025-3-22 11:58
https://doi.org/10.1007/978-3-8348-9038-2consisting of documents, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general domain corpora, and 2) the documents are written in a language other than English. Unlike expectations, the state-of-the-art transform作者: 恃強(qiáng)凌弱的人 時(shí)間: 2025-3-22 13:08 作者: 馬具 時(shí)間: 2025-3-22 20:41
Programmierbare Logikbausteine,om Wikipedia and an answer, derived from the paragraph. The task is to take both the question and a paragraph as input and come up with a yes/no answer, i.e. to produce a binary output. In this paper, we present a reproducible approach to DaNetQA creation and investigate transfer learning methods fo作者: Interlocking 時(shí)間: 2025-3-22 22:18
https://doi.org/10.1007/978-3-8348-9038-2an data. We train topic models using three algorithms (LDA and ARTM – sparse and dense) and evaluate their quality. We compute topic vectors for sentences of a metaphor-annotated Russian corpus and train several classifiers to identify metaphor with these vectors. We compare the performance of the t作者: APEX 時(shí)間: 2025-3-23 01:58
Programmierbare Logikbausteine,mple, in the field of teaching – for the automatic creation of tests for training materials or the enrichment of interaction techniques for question-answering systems. Leading research in this area shows that models based on the Seq2Seq architecture achieve the best quality. However, such models do 作者: 增長(zhǎng) 時(shí)間: 2025-3-23 08:08 作者: COST 時(shí)間: 2025-3-23 11:20 作者: Hot-Flash 時(shí)間: 2025-3-23 14:34
https://doi.org/10.1007/978-3-8348-9038-2stly, developers of book recommendation systems and electronic libraries may be interested in filtering texts by the age of the most likely readers. Further, parents may want to select literature for children. Finally, it will be useful for writers and publishers to determine which features influenc作者: organic-matrix 時(shí)間: 2025-3-23 21:02
Programmierbare Logikbausteine,from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. Emojis contained in the text messages were used to annotate the data for emotions expressed. This paper proposes an integrated approach to text-based emotion classification combining li作者: PRO 時(shí)間: 2025-3-24 00:12
Programmierbare Logikbausteine, Russian. We run an extensive series of experiments of modern extractive and abstractive approaches. The results demonstrate that BERT-based models show modest performance, reaching up?to 0.26 ROUGE-1F-measure. In addition, human evaluation shows that neural approaches could generate feasible althou作者: verdict 時(shí)間: 2025-3-24 05:50
https://doi.org/10.1007/978-3-8348-9370-3hem, particularly between the arguments of a predicate. For this purpose, the RuSentiFrames lexicon was created. But the training of the ML model requires an annotated collection of data, and since the manual annotation is laborious and expensive, the automation of the process is preferable. In this作者: 安定 時(shí)間: 2025-3-24 10:18 作者: Junction 時(shí)間: 2025-3-24 11:50
Programmierbare Logikbausteine,how that the Sequence Generating BERT model achieves decent results in significantly fewer training epochs compared to the standard BERT. We also introduce and experimentally examine a mixed model, an ensemble of BERT and Sequence Generating BERT models. Our experiments demonstrate that the proposed作者: 洞穴 時(shí)間: 2025-3-24 18:45
Analog-Digital- und Digital-Analog-Umsetzer,e suspicious lesions detection stage. Contrary to typical decisions in medical image analysis, the proposed approach considers input data not as a 2D or 3D image, but rather as a point cloud, and uses deep learning models for point clouds. We discovered that point cloud models require less memory an作者: UNT 時(shí)間: 2025-3-24 20:27 作者: Engaged 時(shí)間: 2025-3-25 02:37
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/156376.jpg作者: Infuriate 時(shí)間: 2025-3-25 04:37 作者: HUSH 時(shí)間: 2025-3-25 08:26
978-3-030-72609-6Springer Nature Switzerland AG 2021作者: demote 時(shí)間: 2025-3-25 12:38
https://doi.org/10.1007/978-3-8348-9038-2“Lime explanations”. If deemed unfair, LimeOut then applies feature dropout to obtain a pool of classifiers. These are then combined into an ensemble classifier that was empirically shown to be less dependent on sensitive features without compromising the classifier’s accuracy. We present different 作者: FLAT 時(shí)間: 2025-3-25 16:44
https://doi.org/10.1007/978-3-8348-9038-2seline development, and designing a shared task in hopes of improving the baseline. Eventually, we realize that the current NER and RE technologies are far from being mature and do not overcome so far challenges like ours.作者: 決定性 時(shí)間: 2025-3-25 20:15
https://doi.org/10.1007/978-3-8348-9038-2 theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.作者: Projection 時(shí)間: 2025-3-26 01:14 作者: 錢(qián)財(cái) 時(shí)間: 2025-3-26 07:12
Programmierbare Logikbausteine,agraph model of the text, the metagraph is decomposed into a multipartite graph, which allows its usage in existing models for generating text questions without losing information about the additional hierarchical and semantical dependencies of the text.作者: 遠(yuǎn)足 時(shí)間: 2025-3-26 09:12 作者: Glossy 時(shí)間: 2025-3-26 16:24
Analog-Digital- und Digital-Analog-Umsetzer,ion of the nodule. Therefore, we developed an algorithm for sampling points from a point cloud constructed from a 3D image of the candidate region. The algorithm is able to guarantee the capture of both context and candidate information as part of the point cloud of the nodule candidate. We designed作者: apropos 時(shí)間: 2025-3-26 20:19
Making ML Models Fairer Through Explanations: The Case of LimeOut“Lime explanations”. If deemed unfair, LimeOut then applies feature dropout to obtain a pool of classifiers. These are then combined into an ensemble classifier that was empirically shown to be less dependent on sensitive features without compromising the classifier’s accuracy. We present different 作者: 低位的人或事 時(shí)間: 2025-3-26 21:14
RuREBus: A Case Study of Joint Named Entity Recognition and Relation Extraction from E-Government Doseline development, and designing a shared task in hopes of improving the baseline. Eventually, we realize that the current NER and RE technologies are far from being mature and do not overcome so far challenges like ours.作者: 就職 時(shí)間: 2025-3-27 02:19 作者: 漂浮 時(shí)間: 2025-3-27 09:07
Do Topics Make a Metaphor? Topic Modeling for Metaphor Identification and Analysis in Russianredictors align with the conceptual mappings attested in literature. We also compare the topical heterogeneity of metaphoric and non-metaphoric contexts in order to test the hypothesis that metaphoric discourse should display greater topical variability due to the presence of Source and Target domai作者: vitreous-humor 時(shí)間: 2025-3-27 11:36 作者: 煞費(fèi)苦心 時(shí)間: 2025-3-27 16:27
Automatic Generation of Annotated Collection for Recognition of Sentiment Framesf the algorithm evaluation, based on several different characteristics, were relatively high. The solutions of problematic cases have been suggested and are expected to be implemented in further research.作者: 啞劇 時(shí)間: 2025-3-27 19:01 作者: 向外才掩飾 時(shí)間: 2025-3-28 01:37
Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu作者: Omnipotent 時(shí)間: 2025-3-28 02:43
Programmierbare Logikbausteine,) an NLI task, for which we use the Russian part of XNLI, 3) another question answering task, SberQUAD. For language transferring we use English to Russian translation together with multilingual language fine-tuning.作者: esthetician 時(shí)間: 2025-3-28 08:04
DaNetQA: A Yes/No Question Answering Dataset for the Russian Language) an NLI task, for which we use the Russian part of XNLI, 3) another question answering task, SberQUAD. For language transferring we use English to Russian translation together with multilingual language fine-tuning.作者: 溺愛(ài) 時(shí)間: 2025-3-28 12:58
0302-9743 020, held during October 15-16, 2020. The conference was planned to take place in Moscow, Russia, but changed to an online format due to the COVID-19 pandemic..The 27 full papers and 4 short papers presented in this volume were carefully reviewed and selected from a total of 108 qualified submission作者: 描述 時(shí)間: 2025-3-28 16:07
https://doi.org/10.1007/978-3-8348-9370-3s. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.作者: 開(kāi)玩笑 時(shí)間: 2025-3-28 22:35 作者: Allege 時(shí)間: 2025-3-29 01:52 作者: groggy 時(shí)間: 2025-3-29 03:52 作者: indignant 時(shí)間: 2025-3-29 08:00
Programmierbare Logikbausteine,ads of high-quality media that publishes news in accordance with the classical model. We prove dataset eligibility for training by building an abstractive summarization framework based on pre-trained language models and comparing summarization results with extractive baselines.作者: modish 時(shí)間: 2025-3-29 14:59
https://doi.org/10.1007/978-3-8348-9038-2l word representations outperform previously proposed feature-based models for discourse relation classification. By ensembling both methods, we are able to further improve the performance of the discourse relation classification achieving the new state of the art for Russian.作者: 迅速飛過(guò) 時(shí)間: 2025-3-29 18:58
Abstractive Summarization of Russian News Learning on Quality Mediaads of high-quality media that publishes news in accordance with the classical model. We prove dataset eligibility for training by building an abstractive summarization framework based on pre-trained language models and comparing summarization results with extractive baselines.作者: machination 時(shí)間: 2025-3-29 20:42
RST Discourse Parser for Russian: An Experimental Study of Deep Learning Modelsl word representations outperform previously proposed feature-based models for discourse relation classification. By ensembling both methods, we are able to further improve the performance of the discourse relation classification achieving the new state of the art for Russian.作者: Heretical 時(shí)間: 2025-3-30 01:52
Conference proceedings 2021ers are organized in topical sections as follows: invited papers; natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; and process mining. ..?.作者: 不易燃 時(shí)間: 2025-3-30 07:22 作者: MERIT 時(shí)間: 2025-3-30 11:07 作者: 一致性 時(shí)間: 2025-3-30 12:55 作者: crease 時(shí)間: 2025-3-30 18:16 作者: 同謀 時(shí)間: 2025-3-30 22:05 作者: 軍火 時(shí)間: 2025-3-31 04:10
A Comparative Study of Feature Types for Age-Based Text Classification children’s or adult. We evaluated the following types of features: readability indices, sentiment, lexical, grammatical and general features, and publishing attributes. The results obtained show that the features describing the text at the document level can significantly increase the quality of machine learning models.作者: 越自我 時(shí)間: 2025-3-31 07:26 作者: 我怕被刺穿 時(shí)間: 2025-3-31 12:37
Identifying User Interests and Habits Using Object Detection and Semantic Segmentation Modelslude 90200 photos in total. The accuracy of the developed models is from 83.7% up to 86.6% mAP for object detection depending on a specific category of objects and 78.4% pixel accuracy for segmentation.作者: Misgiving 時(shí)間: 2025-3-31 14:23
