352 research outputs found
Dexter: an open source framework for entity linking
No full textWe introduce Dexter, an open source framework for entity linking. The entity linking task aims at identifying all the small text fragments in a document referring to an entity contained in a given knowledge base, e.g., Wikipedia. The annotation is usually organized in three tasks. Given an input document the first task consists in discovering the fragments that could refer to an entity. Since a mention could refer to multiple entities, it is necessary to perform a disambiguation step, where the correct entity is selected among the candidates. Finally, discovered entities are ranked by some measure of relevance. Many entity linking algorithms have been proposed, but unfortunately only a few authors have released the source code or some APIs. As a result, evaluating today the performance of a method on a single subtask, or comparing different techniques is difficult. In this work we present a new open framework, called Dexter, which implements some popular algorithms and provides all the tools needed to develop any entity linking technique. We believe that a shared framework is fundamental to perform fair comparisons and improve the state of the art
A House in the Form of a City. Casa Ceccarelli in Bologna (1962-63)
No full textThe Casa Ceccarelli in Bologna was designed by Giancarlo De Carlo for the astrophysicist and educator Marcello Ceccarelli in 1961-62, a time when the architect was working on the university settlement Collegio del Colle in Urbino, while his patron was completing the Croce del Nord (Northern Cross) - the first Italian radio telescope - in the Po valley. Born as a sort of experiment between two like-minded and unusual intellectuals, this building was, in De Carlo's words, “a flagrant case of a project-process, or in other words, of architecture” but also a laboratory for studying and testing new spatial inventions in a playful way. The author of this essay has lived in the house since he was a boy, experiencing it as a miniature city surrounded by its countryside and populated by numerous friends who were always there
Twitter anticipates bursts of requests for Wikipedia articles
No full textMost of the tweets that users exchange on Twitter make implicit mentions of named-entities, which in turn can be mapped to corresponding Wikipedia articles using proper Entity Linking (EL) techniques. Some of those become trending entities on Twitter due to a long-lasting or a sudden effect on the volume of tweets where they are mentioned. We argue that the set of trending entities discovered from Twitter may help predict the volume of requests for relating Wikipedia articles. To validate this claim, we apply an EL technique to extract trending entities from a large dataset of public tweets. Then, we analyze the time series derived from the hourly trending score (i.e., an index of popularity) of each entity as measured by Twitter and Wikipedia, respectively. Our results reveals that Twitter actually leads Wikipedia by one or more hours
Learning Relatedness Measures for Entity Linking
No full textEntity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking.
The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms
You should read this! let me explain you why: explaining news recommendations to users
No full textRecommender systems have become ubiquitous in contentbased
web applications, from news to shopping sites. Nonetheless,
an aspect that has been largely overlooked so far in the recommender system literature is that of automatically
building explanations for a particular recommendation.
This paper focuses on the news domain, and proposes to enhance effectiveness of news recommender systems by adding,
to each recommendation, an explanatory statement to help
the user to better understand if, and why, the item can be
her interest. We consider the news recommender system as a
black-box, and generate different types of explanations employing pieces of information associated with the news. In
particular, we engineer text-based, entity-based, and usagebased explanations, and make use of a Markov Logic Networks to rank the explanations on the basis of their effectiveness. The assessment of the model is conducted via a user study on a dataset of news read consecutively by actual users. Experiments show that news recommender systems
can greatly benefit from our explanation module
“Una domanda ai compositori”
No full textLuigi Ceccarelli takes into account the prerogatives of the current technologies referring to the elements pointed out by the critical commentary of the authors quoted inside the proposed “A question to the composers”. Noting that the remarks are more aimed at certain kinds of use, made to cover a lack of ideas, rather than at technology itself, the author points out how, to make music, a level of complexity of higher “logical standard” should be really reached
SEL: A unified algorithm for entity linking and saliency detection
No full textThe Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base, e.g., Wikipedia. Entity Linking has a large impact in several text analysis and information retrieval related tasks. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same relevance and utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algorithm comprehensively addressing both entity linking and saliency detection. The first step is based on a classifier aimed at identifying a set of candidate entities that are likely to be mentioned in the document, thus maximizing the precision of the method without hindering its recall. The second step is still based on machine learning, and aims at choosing from the previous set the entities that actually occur in the document. Indeed, we tested two different versions of the second step, one aimed at solving only the entity linking task, and the other that, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algorithm outperforms state-of-the-art competitors, and is able to detect salient entities with high accuracy
Los vacíos y la evolución de la estructura del universo
No full textTesis (Doctor en Astronomía)--Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física, 2009.En este trabajo de tesis se llevó a cabo un estudio estadístico completo acerca de las regiones subdensas (vacíos) en el Universo. Para tal fin se analizaron las características espaciales y dinámicas de regiones subdensas en simulaciones numéricas cosmológicas, pobladas con galaxias semianalíticas, y catálogos observacionales de galaxias.
Se examinaron las propiedades de las galaxias en paredes de vacíos en catálogos de galaxias (SDSS y 2dFGRS). Los
resultados sugieren una dependencia con el medio a gran escala, posiblemente vinculada al material que se acumula en
las paredes de las regiones subdensas como consecuencia de la expansión. Se analizó el patrón de distorsiones en el
espacio de redshift de la función de correlación cruzada entre vacíos y galaxias, obteniéndose estimas de la velocidad
de expansión de las regiones vacías.por María Laura Ceccarelli
Dynamic Analysis of a Compliant, Parallel and Three-Dimensional Meso-Manipulator Generated from a Planar Structure
No full textAbstract. The dynamic study of a parallel meso-manipulator, characterized by flexure hinge joints and by an original planar structure, is addressed in this paper under small displacements. This work has to be considered as the continuation of a previous paper, developing the kinematic study and the workspace analysis of the same structure[1].
The present paper deals with the dynamic analysis of the meso-manipulator: as first outcome, a dynamic response strictly related to the implemented motion profile emerges from the simulations results. A noise contribute has been thereafter introduced into the symmetric trapezoidal profile to simulate a real-world behaviour, and also the compliance performances of the robot are evaluated with this noise force.
[1] Amici, C., Borboni, A., Magnani, P.L., Pomi, D. (2008) Kinematic Analysis of a Compliant Parallel and Three-Dimensional Meso-Manipulator Generated from a Planar Structure. 2nd European Conference on Mechanism Science EUCOMES2008, Cassino, Ital
Differential analysis of Operating System indicators for anomaly detection in dependable systems: An experimental study
No full textDependable complex systems often operate under variable and non-stationary conditions, which requires efficient and extensive monitoring and error detection solutions. Among the many, the paper focuses on anomaly detection techniques, which monitor the evolution of some specific indicators through time to identify anomalies, i.e. deviations from the expected operational behavior. The timely identification of anomalies in dependable, fault tolerant systems allows to timely detect errors in the services and react appropriately. In this paper, we investigate the possibility to monitor the evolution of indicators through time using the random walk model on indicators belonging to Operating Systems, specifically in our study the Linux Red Hat EL5. The approach is based on the experimental evaluation of a large set of heterogeneous indicators, which are acquired under different operating conditions, both in terms of workload and faultload, on an air traffic management target system. The statistical analysis is based on a best-fitting approach aiming to minimize the integral distance between the empirical data distribution and some reference distributions. The outcomes of the analysis show that the idea of adopting a random walk model for the development of an anomaly detection monitor for critical systems that operates at Operating System level is promising. Moreover, standard distributions such as Laplace and Cauchy, rather than Normal, should be used for setting up the thresholds of the monitor. Further studies that involve a new application, a different Operating System and a new layer (an Application Server) will allow verifying the generalization of the approach to other fault tolerant systems, monitored layers and set of indicators
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