1,720,996 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Folksonomy Based Question Answering System
No full textFinancial data is on the rise. Most of this data is unstructured in nature. A major contributor to this
data is in the form of web articles. With an increase in such data, there is a need for techniques that
parse unstructured information. This thesis project presents an overview of a folksonomy-based
approach for a Question Answering system. The proposed system is divided into two steps, the
first step processes the contextual information using techniques such as document-to-vector, tf-idf
and topic modelling which forms the level 1 granularity. The variant of word2vec in the form of
paragraph2vec is used for achieving a sentence level granularity (level 2). Various combinations
of level 1 and level 2 granularity are explored, and the best combination is sought after. The
concepts of folksonomy, which is social and contextual tagging, is associated with reduction of
search space. The search space is a combination of all possible answers in which the correct answer
resides. The idea is to reduce the search space such that different algorithms have the ability of
finding the correct answers. The models are then stress tested by varying different parameters. The
parameters are obtained after performing a grid search. While finding the best model, more than
12,000 models were generated and tested. The best model was tested on two test cases where it
generated an accuracy of 61% and 64%
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
Neural Network Models for Text Understanding
No full textText understanding is a key component of Natural Language Processing (NLP) and Artificial Intelligence (AI). The traditional approaches to solve this problem are by joining the
features selected from sentences and the relevant world knowledge together to make further
inference. However, such feature based methods not only require a great deal of human
effort, but also can only represent the lexical and syntactic information of text and the semantic meanings of text cannot be well represented. Hence, we present novel neural language
models that represent text as semantic vector and apply them to solve multiple high-level
text understanding tasks. The neural language models not only provide effective and general
representations for learning the semantics of text, but also can obtain state-of-the-art performance on a variety of text understanding tasks. In this dissertation, we studied several
Deep Learning techniques, including multi-task learning, transfer learning and multi-lingual
learning and applied them to solve a variety of text understanding tasks, such as Semantic
Textual Similarity, Textual Entailment and Semantic Relation Extraction.
First, we explored the multi-task learning models for Semantic Relatedness task and Textual Entailment task. In Natural Language Processing, there exist several tasks that are
highly related to each other. Instead of using single task learning that optimize each task
specific system independently, we can optimize all tasks simultaneously using one multi-task
learning model. We selected two related NLP tasks, Semantic Relatedness task and Textual
Entailment task, and trained them jointly with a variety of multi-task learning models. This
study explored if multi-task learning can outperform the performance of single-task learning
on related NLP tasks.
Second, we studied the transfer learning models for Semantic Relation Classification task.
Domain adaptation is a common issue in Natural Language Processing, due to the fact that
creating corpora in novelty domain requires a large human annotation effort. In neural
language models, researchers made several attempts to find universal sentence embedding
methods, aiming to obtain general-purpose sentence embeddings that can be widely adopted
to a wide range of NLP tasks. We conducted an experiment to evaluate if transfer learning
can help to train general-purpose sentence encoders for Relation Classification task using a
limited-size of training data.
Last, we studied the neural language models in multi-lingual domains. Specifically, we
created models to solve the Semantic Relation Extraction task for Chinese and Semantic
Textual Similarity task for Arabic, English and Spanish. We aim to show that, compared
to traditional feature based methods, neural language models can (1) achieve state-of-theart performance without any or very few manually designed features, and (2) learn general
sentence representation regardless of languages and domains.ALC added metadata and UTD Seal to title page and corrected item metadata. Filenames were changed according to Treasures policy
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Discourse Parsing and its Application to Question Generation
No full textReading comprehension can be analyzed from three points of view: Semantics, Assessment,
and Cognition. Here, Semantics refers to the task of identifying discourse relations in text.
Assessment involves utilizing these relations to obtain meaningful question-answer pairs.
Cognition means categorizing questions according to their difficulty or complexity levels.
This dissertation addresses how to leverage or design natural language processing tools to
perform underlying tasks and ultimately craft a reading comprehension quiz for use in a
classroom environment.
Previous research has focused on mining shallow, sentence-level semantic relations and using
them to craft intra-sentential, factoid questions. These are not very consequential in the
context of large documents as they do not address how sentences coherently come together
to comprise the full text. Discourse relations are capable of providing a comprehensive
view of the text as they look beyond sentences. These relations focus on how sentences are
logically and structurally linked to each other and provide a summarized, high-level overview
of the document’s semantics. Likewise, one can expect inter-sentential questions generated
using discourse relations to be deep and inferential that can test comprehension abilities
like analysis of the document’s structure, identification of author’s intent, and evaluation
of stated arguments, among others. Testing these abilities allows one to assess student
interpretation of a text effectively.
A deep multi-task learning framework is suggested that accurately deduces high-level discourse relations between text spans. The framework uses structure, syntax, and contextaware text representations that are robust enough to capture the document’s meaning and
intent. A set of syntactic transformations and well-formed transformation templates convert relations into question-answer pairs: the proposed model generates questions that are
grammatically valid and intricate enough to gauge text comprehension. Then, a rich, featuredriven classifier categorizes these questions according to their difficulty levels. Results obtain
empirically show that inter-sentential questions that test the ability to deduce high-level semantic relations in the text are more complex and meaningful than intra-sentential ones.
These modules are linked into a pipeline. The pipeline’s performance is evaluated on benchmark corpora and it is shown that this pipeline can generate high-quality question-answer
pairs that are more purposeful than human-designed ones and ones obtained from previously
designed systems. By enhancing reading comprehension datasets with such questions, one
can hope to advance research in question answering and reading comprehension
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