POS Tagging and its Applications for Mathematics

Content analysis of scientific publications is a nontrivial task, but a useful and important one for scientific information services. In the Gutenberg era it was a domain of human experts; in the digital age many machine-based methods, e.g., graph analysis tools and machine-learning techniques, have been developed for it. Natural Language Processing (NLP) is a powerful machine-learning approach to semiautomatic speech and language processing, which is also applicable to mathematics. The well established methods of NLP have to be adjusted for the special needs of mathematics, in particular for handling mathematical formulae. We demonstrate a mathematics-aware part of speech tagger and give a short overview about our adaptation of NLP methods for mathematical publications. We show the use of the tools developed for key phrase extraction and classification in the database zbMATH

Two new species of Camallanus (Nematoda: Camallanidae) from freshwater turtles in Queensland, Australia.

We describe 2 new species of Camallanus (Nematoda: Camallanidae) from freshwater turtles collected in Queensland, Australia: Camallanus nithoggi n. sp. from Elseya latisternum (Gray) and Camallanus waelhreow n. sp. from Emydura krefftii (Gray), Emydura macquarrii (Gray), and Em. macquarrii dharra Cann. The only Camallanus sp. previously reported from turtles is C. chelonius Baker, 1983 (all other species in the family have been transferred to Serpinema). The 2 new species described here differ from C. chelonius in the number of male preanal papillae (7 vs. 6 in C. chelonius), the number of male postanal papillae (5 vs. 4 in C. chelonius), and the number of buccal capsule ridges. Additionally, we removed the tissues overlying the buccal capsule and used scanning electron micrographs (SEM) to show that the peribuccal shields extend laterally from the buccal capsule, the basal ring is separated from the buccal capsule by a narrow isthmus, and there is a buttress along the lateral margin of the buccal capsule that has not previously been observed in species of Camallanus

Adapting Decision DAGs for Multipartite Ranking

European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010Multipartite ranking is a special kind of ranking for problems in which classes exhibit an order. Many applications require its use, for instance, granting loans in a bank, reviewing papers in a conference or just grading exercises in an education environment. Several methods have been proposed for this purpose. The simplest ones resort to regression schemes with a pre- and post-process of the classes, what makes them barely useful. Other alternatives make use of class order information or they perform a pairwise classi cation together with an aggregation function. In this paper we present and discuss two methods based on building a Decision Directed Acyclic Graph (DDAG). Their performance is evaluated over a set of ordinal benchmark data sets according to the C-Index measure. Both yield competitive results with regard to stateof- the-art methods, specially the one based on a probabilistic approach, called PR-DDA

Mutual synchronization and clustering in randomly coupled chaotic dynamical networks

We introduce and study systems of randomly coupled maps (RCM) where the relevant parameter is the degree of connectivity in the system. Global (almost-) synchronized states are found (equivalent to the synchronization observed in globally coupled maps) until a certain critical threshold for the connectivity is reached. We further show that not only the average connectivity, but also the architecture of the couplings is responsible for the cluster structure observed. We analyse the different phases of the system and use various correlation measures in order to detect ordered non-synchronized states. Finally, it is shown that the system displays a dynamical hierarchical clustering which allows the definition of emerging graphs.Comment: 13 pages, to appear in Phys. Rev.

Classification of protein interaction sentences via gaussian processes

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption

Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the futureThis work has partially been supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596 and TIN2009–14205, the Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–04, and the Generalitat Valenciana under grant PROMETEO/2010/02