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El trabajo sobre hueso (arqueozoologia, métodos e industria) para el conocimiento de la prehistoria en Asturias

  • Adán Álvarez, Gema Elvira
Se dará a conocer la selección de la materia prima ósea y las formas de transformación antrópicas en la Asturias prehistórica. El conjunto final se denomina Industria Ósea. El trabajo desde su fabricación hasta su exhumación en cavidades princi
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Un siglo de hallazgos: evidencias arqueozoológicas de origen marino en el Paleolítico superior asturiano

  • Álvarez Fernández, Esteban
La presencia de restos arqueozoológicos de origen marino en los yacimientos paleolíticos asturianos se documenta a partir del primer tercio del siglo XX, cuando se practican excavaciones arqueológicas en cuevas como Cueto de La Mina, La Paloma, La Pe
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El Capitán General Gutiérrez Mellado : un hombre austero, inteligente y bueno

  • Puell de la Villa, Fernando
  • Palacios, Juan Manuel
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Knowledge capture and textual inference

  • Cabaleiro Barciela, Bernardo
The present and future information needs of the society rely on the ability of computers to understand and manage knowledge. The lack of this mechanism explains the problems of knowledge driven systems to effectively perform tasks as question answering and machine reading. One of the biggest bottlenecks is the automatic knowledge acquisition problem. In the actual stage of development, it seems obvious that only semisupervised or unsupervised techniques can scale to deal with large corpora of natural language like the Web. The trend has evolved from populating a predefined ontology to expressing knowledge through either unconstrained relations or propositions. The arrival of new deep language processing technologies let us think that we can annotate large collections of text with accurate predicates that can be used to extracting knowledge from text without tying it to any predefined logical schema. On the other hand, it is not clear which tasks can harness this knowledge and how it can be done. This master’s thesis proposes a new method of knowledge capture and textual inference based on three cornerstones: (1) First, we develop a procedure to turn plain text into a graph based representation taking advantage of existing tools. (2) Second, we develop a proposition extraction system. (3) Lastly, we study an unsupervised method for correction of appositive dependencies, as an example of the textual inferences that the generated proposition store enables. In addition, we generate two useful resources for future tasks of natural language processing: A corpus of 7 million documents represented as semantically enriched graphs and a proposition store of semantic classes with 8 million instances of entity-class relations.
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Community Structure in Endorsement Social Networks

  • Garrido Yuste, Guillermo
In this work, we study the community structure of endorsement networks, i.e., social networks in which a directed edge u → v is asserting an action of support from user u to user v. Examples include scenarios where a user u is favouring a photo, liking a post, or following the microblog of user v. Very often, endorsement networks are sub- networks of more complex social systems; for instance, a photo-sharing site typically includes a “favouring” function, which induces an endorsement network. We start from the hypothesis that the footprint of a community in an endorsement network is a bipartite directed clique from a set of followers to a set of leaders, and apply frequent itemset mining techniques to discover such bicliques. Our analysis of real networks indicated that, with high statistical significance, this hypothesis holds, and that the leaders of a community are endorsing each other forming a very dense nucleus. Our method produces many similar bicliques, which are different footprints of the same community. Thus, we propose a novel clustering technique in order to coalesce similar bicliques into meaningful communities. We explore different similarity measures based on set similarity and on edge density between followers and leaders, and by expressing edge density as an inner product operation we show how to make the clustering algo- rithm scalable. Our experiments demonstrate that our clustering algorithm is capable of discovering communities characterised by a set of leaders who link to each other and followers who link to the leaders.
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