Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Treebanks. In this paper, we investigate the extension of those methods while considerably improved the runtime and training time efficiency via L2SVMs. We also present several properties and constraints to enhance the parser completeness in runtime. We further in...

It is shown that for a large class of non-holonomic quantum mechanical systems one can make the computation of BRST charge fully algorithmic. Two computer algebra programs written in the language of REDUCE are described. They are able to realize the complex calculations needed to determine the charge for general nonlinear algebras. Some interesting specific solutions are discussed.

This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. T...

Transition-based approaches have shown competitive performance on constituent and dependency parsing of Chinese. Stateof-the-art accuracies have been achieved by a deterministic shift-reduce parsing model on parsing the Chinese Treebank 2 data (Wang et al., 2006). In this paper, we propose a global discriminative model based on the shift-reduce parsing process, combined with a beam-search decod...

The production of municipal waste is increasing all over the world. Although a significant part collected as commingled waste, much it recyclable if disposed properly. Thus, separate deposition and collection plays an extremely important role today, more than ever, not only in terms preventing pollution but also from point view recycling driver circular economy efficient use resources. This wor...