Search

The non-marine diatom flora of the Antarctic continent is currently being revised as recent work within the Maritime and Sub-Antarctic regions has unveiled a number of new endemic species. The under reporting of endemic species is partially due to the historic 'force-fitting' of Antarctic species into European names. Within East Antarctica, the Vestfold Hills are an extensively studied area known for their diversity of lakes spanning a salinity gradient, for which Diatoms of the saline lakes of the Vestfold Hills, Antarctica by Roberts & McMinn (1999) has a primary identification resource for over two decades. To determine if improved taxonomic resolution alters the ecological interpretation of these lakes, 30 prepared lake sediment samples first examined by Roberts & McMinn (1999) were re-analysed using an updated, fine-grained taxonomic approach. Our analyses confirmed the role of salinity and alkalinity in structuring Vestfold Hills diatom communities. Thus, the increased taxonomic resolution did not change the original ecological interpretation. However, the revised flora of the Vestfold Hills features 37 species endemic to the Antarctic Region, including members of Luticola, Sabbea and Halamphora, among others. In total, 183 taxa across 38 genera were observed, in contrast to the 67 taxa reported in the original work. The revised flora, including a larger proportion of endemic species, may improve our understanding of the diatom community in general and has wider implications for both conservation and biodiversity estimates of Antarctica. More widely applied, these data can be incorporated into larger studies of Antarctic microbial biogeography and biodiversity. [ABSTRACT FROM AUTHOR]

Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro- F1, macro- F1 and weighted- F1. [ABSTRACT FROM AUTHOR]