1,720,963 research outputs found
Crowdsourcing large scale wrapper inference
Full text linkWe present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate.We present a crowdsourcing system for large-scale production of accurate wrappers to extract data from data-intensive websites. Our approach is based on supervised wrapper inference algorithms which demand the burden of generating training data to workers recruited on a crowdsourcing platform. Workers are paid for answering simple queries carefully chosen by the system. We present two algorithms: a single worker algorithm (ALFη) and a multiple workers algorithm (alfred). Both the algorithms deal with the inherent uncertainty of the workers’ responses and use an active learning approach to select the most informative queries. alfred estimates the workers’ error rate to decide at runtime how many workers should be recruited to achieve a quality target. The system has been fully implemented and tested: the experimental evaluation conducted with both synthetic workers and real workers recruited on a crowdsourcing platform show that our approach is able to produce accurate wrappers at a low cost, even in presence of workers with a significant error rate
ALFRED: crowd assisted data extraction
No full textThe development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accu- racy can be achieved by supervised approaches, but the costs of training data, i.e., annotations over a set of sam- ple pages, limit their scalability. Crowdsourcing platforms are making the manual annotation process more affordable. However, the tasks demanded to these platforms should be extremely simple, to be performed by non-expert people, and their number should be minimized, to contain the costs. We demonstrate alfred, a wrapper inference system super- vised by the workers of a crowdsourcing platform. Training data are labeled values generated by means of membership queries, the simplest form of queries, posed to the crowd. alfred includes several original features: it automatically selects a representative sample set from the input collection of pages; in order to minimize the wrapper inference costs, it dynamically sets the expressiveness of the wrapper for- malism and it adopts an active learning algorithm to select the queries posed to the crowd; it is able to manage inaccu- rate answers that can be provided by the workers engaged by crowdsourcing platforms
Big data linkage for product specification pages
No full textAn increasing number of product pages are available from thousands of web sources, each page associated with a product, containing its attributes and one or more product identifiers. The sources provide overlapping information about the products, using diverse schemas, making web-scale integration extremely challenging. In this paper, we take advantage of the opportunity that sources publish product identifiers to perform big data linkage across sources at the beginning of the data integration pipeline, before schema alignment. To realize this opportunity, several challenges need to be addressed: identifiers need to be discovered on product pages, made difficult by the diversity of identifiers; the main product identifier on the page needs to be identified, made difficult by the many related products presented on the page; and identifiers across pages need to be resolved, made difficult by the ambiguity between identifiers across product categories. We present our RaF (Redundancy as Friend) solution to the problem of big data linkage for product specification pages, which takes advantage of the redundancy of identifiers at a global level, and the homogeneity of structure and semantics at the local source level, to effectively and efficiently link millions of pages of head and tail products across thousands of head and tail sources. We perform a thorough empirical evaluation of our RaF approach using the publicly available Dexter dataset consisting of 1.9M product pages from 7.1k sources of 3.5k websites, and demonstrate its effectiveness in practice
Minimizing the Costs of the Training Data for Learning Web Wrappers
No full textData extraction from the Web represents an important issue. Several approaches have been developed to bring the wrapper generation process at the web scale. Although they rely on different techniques and formalisms, they all learn a wrapper given a set of sample pages. Unsupervised approaches require just a set of sample pages, supervised ones also need training data. Unfortunately, the accuracy obtained by unsupervised techniques is not sufficient for many applications. On the other hand, obtaining training data is not cheap at the web scale. This paper addresses the issue of minimizing the costs of collecting training data for learning web wrappers. We show that two interleaved problems affect this issue: the choice of the sample pages, and the expressiveness of the wrapper language. We propose a solution that leverages contributions in the field of learning theory, and we discuss the promising results of an experimental evaluation of our approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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