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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constitute

蚊帳

過分自信

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

staging

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

恃強(qiáng)凌弱的人

馬具

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

Interlocking

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

APEX

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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