In this paper, we explore the possibility that machine learning approaches to naturallanguage processing (NLP) being developed in engineering-oriented computational linguistics (CL) may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning (ML) results could inform basic debates about language, in one area at least, and ...

In this paper, we explore the possibility that machine learning approaches to naturallanguage processing (NLP) being developed in engineering-oriented computational linguistics (CL) may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning (ML) results could inform basic debates about language, in one area at least, and ...

Machine learning (ML) and genetic algorithm (GA) are well known techniques which can be used to calibrate environmental models. This paper investigates the calibration of the 2-D horizontal, vertically mixed lake models OneLay and PolTra using ML and GA routines. A GA was used jointly with the Open Modelling Interface (OpenMI) wrapper approach on a single powerful server. Explicit and implicit ...

In recent days, liveness detection of finger print image has become very essential in recognition systems because fake prints are used lieu real prints. Many machine learning(ML) techniques have been widely for non live these provide high accurate identification and also cost effective. These enhance the accuracy classification spoof images. this article , literature review is done about learni...

I’ve been using machine-learning methods for more than two decades for a variety of problems, the most challenging of which has been to design a set of algorithms that learn from data how to trade securities profitably on their own and with minimal human input. I started this research to answer a basic question, namely, whether machine-learning algorithms could be shaped to make investment deci...

The paper deals with the task of definition extraction from a small and noisy corpus of instructive texts. Three approaches are presented: Partial Parsing, Machine Learning and a sequential combination of both. We show that applying ML methods with the support of a trivial grammar gives results better than a relatively complicated partial grammar, and much better than pure ML approach.

. In the paper different architectures with partly self-developed simulation packages are described illustrating the benefits of combining simulation and machine learning (ML) techniques in manufacturing. From the artificial intelligence (AI) and ML side, artificial neural networks, heuristic search, simulated annealing, and agent-based techniques are put into action. The applicability of the p...

We propose a novel ensemble approach chemical named entity recognition (CNER) tool that uses different CNER tools such as OSCAR4 and ChemSpot with different characteristics by using machine learning (ML) technique. Since this tool may identify typical errors of one CNER by using other tools’ output, our system outperforms ChemSpot (ML-based) and OSCAR4 (rule-based) in original setting.

In the paper different architectures with partly self-developed simulation packages are described illustrating the benefits of combining simulation and machine learning (ML) techniques in manufacturingintelligence (AI) and ML side, artificial neural networks, heuristic search, simulated annealing, and agent-based techniques are put into action. The applicability of the proposed solutions is ill...

Abstract. High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term changes. Satellite instruments provide measurements over typical mission durations 5–15 years. Various methodologies have then been applied to merge homogenise the different satellite in order create observation-based with minimal gaps. However, individual ...