Empirical methods in Natural Language Processing (NLP) and Machine Translation (MT) have become mainstream in the research field. Accordingly, it is important that the tools and techniques in these paradigms be taught to potential future researchers and developers in University courses. While many dedicated courses on Statistical NLP can be found, there are few, if any courses on Empirical Appr...

We describe our work in the EMNLP 2016 second code-switching shared task; a generic language independent framework for linguistic code switch point detection (LCSPD). The system uses characters level 5-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We participated in the Modern Standard Arabic (MS...

We present a fully automatic method for content selection evaluation in summarization that does not require the creation of human model summaries. Our work capitalizes on the assumption that the distribution of words in the input and an informative summary of that input should be similar to each other. Results on a large scale evaluation from the Text Analysis Conference show that input-summary...

This paper describes the statistical machine translation system developed at RWTH Aachen University for the English→German and German→English translation tasks of the EMNLP 2017 Second Conference on Machine Translation (WMT 2017). We use ensembles of attention-based neural machine translation system for both directions. We use the provided parallel and synthetic data to train the models. In add...

This study describes fragmentation of silicon clusters of size N ≤ 40. Fragmentation is produced by the increase of the kinetic energy Ek of the nuclear system and a time-dependent Hartree-Fock method, with a semi-empirical Hamiltonian is used for the evaluation of the transient ensuing the energy input. A typical channel of fragmentation has been observed which consists on the emission of smal...

In this paper we investigate techniques for semi-supervised learning that split their unsupervised and supervised components — that is, an initial unsupervised phase is followed by a supervised learning phase. We first analyze the relative value of labeled and unlabeled data. We then present methods that perform “split” semi-supervised learning and show promising empirical results.

The Methotrexate delivery by carbon nanotubes (CNTs) and the structural changes of drugcombination upon the carbon nanotubes and bio thermodynamic of the drug have been studied by molecularcomputational methods. Computational molecular methods have been fulfilled by molecular mechanics methods with four force field, and semi empirical with all methods. We investigate different param...

The conferences ACL (Association for Computational Linguistics) and EMNLP (Empirical Methods in Natural Language Processing) rank among the premier venues that track the research developments in Natural Language Processing and Computational Linguistics. In this paper, we present a study on the research papers of approximately two decades from these two NLP conferences. We apply keyphrase extrac...