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Titlebook: Analysis of Images, Social Networks and Texts; 9th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu Conference proceedin

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期刊全称Analysis of Images, Social Networks and Texts
期刊简称9th International Co
影响因子2023Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Analysis of Images, Social Networks and Texts; 9th International Co Wil M. P. van der Aalst,Vladimir Batagelj,Elena Tu Conference proceedin
影响因子This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, 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 submissions. The papers 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. .. .
Pindex Conference proceedings 2021
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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
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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
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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
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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
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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
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