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Leveraging Social Network Data for Analytical CRM Strategies - The Introduction of Social BI.
(2012)
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Introduction of RePriCo’13
(2013)
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
On 1st January 1998, the German telecom market was fully liberalised. Since then genuine competition between market participants has developed, based on a comprehensive legal and regulatory framework that provides for safeguards against unfair competition and market power by Deutsche Telekom. Today, about 10 years after the liberalisation of the telecommunications sector a revision of this regulatory approach has become necessary because at least on three dimensions the situation is quite different from the one 10 years ago: First, with numerous established alternative operators in the market monopolies have been successfully challenged and competition introduced. Second, not only is Cable TV becoming in large parts of Germany a viable alternative for the provision of broadband services but also mobile services are becoming increasingly a substitute for fixed services. Last but not least there are important technological changes under way, requiring huge investments in infrastructure upgrades for next generation networks. In the light of these new developments the question is to which extent the current regulatory approach of severe ex-ante regulatory intervention is still appropriate. Is any part of the network of the former incumbent still a bottleneck? A more light handed regulatory approach might be the right response to this new situation. The paper is organised as follows: The first section will briefly examine the economic rationale for regulating network access. Based on the assumption that regulation is always necessary when bottlenecks exist regulatory principles for an efficient network access regime will be derived. The second section compares the situation of the German market in early 1998 with the one of today. Thereby three dimensions will be considered: the degree of competition, the potential for substitution and technological developments. The third section will define some requirements for the future regulation of telecom markets. Proposals will be elaborated how to ensure competitive telecom markets in the light of new economic and technological challenges.