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

Enriching Frame Representations with Distributionally Induced Senses

We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora. These features are extracted from distributionally induced sense inventories and subsequently linked to the manually-constructed frame representations to boost the performance of frame disambiguation in context. Since Framester is a frame-based knowledge graph, which enables full-fledged OWL querying and reasoning, our resource paves the way for the development of novel, deeper semantic-aware applications that could benefit from the combination of knowledge from text and complex symbolic representations of events and participants. Together with the resource we also provide the software we developed for the evaluation in the task of Word Frame Disambiguation (WFD).Comment: In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan. ELR

Improving Hypernymy Extraction with Distributional Semantic Classes

In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using distributional semantics and using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On the one hand, this allows us to filter out wrong extractions using the global structure of distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction in terms of both precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japa