1,721,194 research outputs found
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Extending Wikification: Nominal discovery, nominal linking, and the grounding of nouns
Full text linkMention discovery, entity linking, and grounding are crucial steps in natural language understanding. Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In this thesis, we address those problems by extending the Illinois Cross-lingual Wikifier with nominal linking and sense disambiguation. We train a nominal detector with the dictionary post-process to discover nominal mentions and classify them into predefined type categories. For the nominal linking, we propose a co-reference model that captures the pairwise features between the named entity and the nominal, and we integrate it with several linking heuristics. Finally, we ground nouns to their Wikipedia titles by adjusting the ranker of the Wikifier with extra features and the training on common nouns. Our proposed approaches show competitive performances on the benchmark datasets.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01The student, Liang-Wei Chen, accepted the attached license on 2017-12-04 at 22:36.The student, Liang-Wei Chen, submitted this Thesis for approval on 2017-12-04 at 22:54.This Thesis was approved for publication on 2017-12-05 at 15:57.DSpace SAF Submission Ingestion Package generated from Vireo submission #11705 on 2018-03-13 at 09:55:38Made available in DSpace on 2018-03-13T15:21:06Z (GMT). No. of bitstreams: 3
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Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Fine-grained entity typing system - design and analysis
Full text linkNamed entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01The student, Pavankumar Reddy Muddireddy, accepted the attached license on 2018-04-23 at 17:30.The student, Pavankumar Reddy Muddireddy, submitted this Thesis for approval on 2018-04-23 at 17:42.This Thesis was approved for publication on 2018-04-24 at 09:20.DSpace SAF Submission Ingestion Package generated from Vireo submission #12436 on 2018-08-31 at 17:21:20Made available in DSpace on 2018-09-04T20:36:52Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-04-24Embargo set by: Seth Robbins for item 107298
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Improvements and augmentations to Learning Based Java: a Java based learning based programming language
Full text linkMachine Learning (ML) is the science that enables computers with the ability to learn without being explicitly programmed. ML is so pervasive today, with applications in speech recognition, recommendation systems, fraud detection and many more that we may not be aware of. To facilitate a rapid pace of development, it is important to create a framework with modularity and reusability. Learning Based Java (LBJava) was introduced by Cognitive Computation Group (CCG) to achieve such goal.
This thesis extends and introduces multiple components in LBJava. We begin by giving a comprehensive literature review relates to Learning Based Programming (LBP) and LBJava.
Then we introduce regression evaluation metrics to LBJava. In addition, we introduce Adaptive Sub- Gradient (AdaGrad) for regression. Then we add a comprehensive tutorial with example on regression. Furthermore, we extend both SGD and AdaGrad algorithms for classification. Then we evaluate across var- ious learning algorithms, with sparse and dense features, using large programmatically generated datasets.
Moreover, we introduce Neural Network (NN), in particular, Multilayer Perceptron (MLP), to LBJava. We also did some miscellaneous work.
Lastly, we conclude on all the extended and added components and provide recommendations for future work.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01The student, Yiming Jiang, accepted the attached license on 2016-04-25 at 16:00.The student, Yiming Jiang, submitted this Thesis for approval on 2016-04-25 at 16:12.This Thesis was approved for publication on 2016-04-26 at 15:44.DSpace SAF Submission Ingestion Package generated from Vireo submission #9486 on 2016-07-07 at 13:50:51Made available in DSpace on 2016-07-07T20:28:02Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-04-26Embargo set by: Seth Robbins for item 93179
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 93179 on 2018-07-08T09:15:20Z
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