Abstract
Automatic morphosyntactic tagging of corpora is usually imperfect. Wrong or strange tagging may be automatically repeated following some patterns. It is usually hard to manually detect all these errors, as corpora may contain millions of tags. This paper presents an approach to detect sequences of part-of-speech tags that have an internal cohesiveness in corpora. Some sequences match to syntactic chunks or correct sequences, but some are strange or incorrect, usually due to systematically wrong tagging. The amount of time spent in separating incorrect bigrams and trigrams from correct ones is very small, but it allows us to detect 70% of all tagging errors in the corpus.

Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.