Titlebook: Hybrid Approaches to Machine Translation; Marta R. Costa-jussà,Reinhard Rapp,Bogdan Babych Book 2016 Springer International Publishing Swi

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Machine Learning Applied to Rule-Based Machine Translationlve morphologically ambiguous verb forms if a rule-based decision is not possible due to parsing or tagging errors. The rule-based core system has a set of rules to decide, based on context information, which verb form should be generated in the target language. However, if the parse tree is not cor

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Creating Hybrid Dependency Parsers for Syntax-Based MTing structures. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. In this chapter, we will discuss how different forms and different hybrid combinations of dependency parses effect the overall

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Using WordNet-Based Word Sense Disambiguation to Improve MT Performancense Disambiguation (WSD) and comparing its results to three MT systems. Our experiments are performed for the English-Slovene language pair using UKB, a freely available graph-based word sense disambiguation system. Since the fine granularity of WordNet is often reported as problematic, we compare t

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reasonable travel plans. Current mainstream traffic forecasting models are developed based on spatial-temporal graph convolutional neural networks, in which appropriate graph structures must be generated in advance. However, most existing graph generation approaches learn graph structures based on

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systems are based on recognizing Activities of Daily Living (ADLs) for providing a suitable decision. Activity recognition systems are currently employed in developing many smart technologies (e.g., smart mobile phone) and their uses have been increased dramatically with availability of Internet of