Development of mass spectrometric techniques applicable to the search for organic matter in the lunar crust

Data processing techniques were developed to measure with high precision and sensitivity the line spectra produced by a high resolution mass spectrometer. The most important aspect of this phase was the interfacing of a modified precision microphotometer-comparator with a computer and the improvement of existing software to serve the special needs of the investigation of lunar samples. In addition, a gas-chromatograph mass spectrometer system was interfaced with the same computer to allow continuous recording of mass spectra on a gas chromatographic effluent and efficient evaluation of the resulting data. These techniques were then used to detect and identify organic compounds present in the samples returned by the Apollo 11 and 12 missions

GermEval 2014 Named Entity Recognition Shared Task: Companion Paper

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems

GermEval 2014 Named Entity Recognition Shared Task: Companion Paper

This paper describes the GermEval 2014 Named Entity Recognition (NER) Shared Task workshop at KONVENS. It provides background information on the motivation of this task, the data-set, the evaluation method, and an overview of the participating systems, followed by a discussion of their results. In contrast to previous NER tasks, the GermEval 2014 edition uses an extended tagset to account for derivatives of names and tokens that contain name parts. Further, nested named entities had to be predicted, i.e. names that contain other names. The eleven participating teams employed a wide range of techniques in their systems. The most successful systems used state-of-the- art machine learning methods, combined with some knowledge-based features in hybrid systems

Unsupervised Sense-Aware Hypernymy Extraction

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austri