1,721,019 research outputs found
A Novel aspect taxonomy and aspect extraction methodology for scholarly book reviews
No full textMany people decide on the quality of a product based on its online reviews, which is also the most commonly used method when purchasing books from online book stores. Compared to other products, a scholarly book is one of the most difficult products to purchase online since customers have limited access to its internal content. Therefore, a customer has to go through multiple reviews in order to get insight on the book. However, the sheer volume of online reviews makes it difficult for a human to process and extract all the meaningful information in order to make an educated purchase. As a result, a requirement for a sentiment analysis system for scholarly book reviews are much needed at this stage. A more accurate opinion of the book can be obtained through aspect-based summarization. This type of summarization of opinions is critical for scholarly book reviews since content, organization, and other features interpret whether the book can be recommended to a customer at a certain education level.
Compared to sentiment analysis on reviews of products/services such as movies or restaurants, there is no well-defined research in aspect extraction or aspect-based sentiment analysis of scholarly book reviews. Not surprisingly for this domain, there is no well-defined aspect taxonomy or an annotated dataset available to extract aspects or to identify aspect categories. Compared to other domains, identifying aspects of book reviews is difficult since aspects such as the quality of the book or the discussed topics always appear implicitly in reviews.
The main contribution of this research is to identify potential aspects and an aspect taxonomy for scholarly book reviews. We also present a (1.) dependency rule-based unsupervised model for aspect extraction, which works better than state-of-the-art unsupervised methods, and (2.) a clustering-based aspect category identification method. Both of these are important first steps for aspect-based sentiment analysis.
The aspect taxonomy for scholarly book reviews is a hierarchical model. Book and Author have been identified as the first level of the taxonomy. Readability, content, worthiness and price, are the next level of aspect taxonomy under the book aspect category. Author expertise has been identified as an aspect category under author. In order to validate the aspect taxonomy, an unsupervised aspect extraction and clustering algorithm is proposed. An existing dependency rule-based aspect extraction algorithm is improved by adding new rules that extract aspects from book reviews. Two existing clustering algorithms for aspect clustering are merged to obtain a new clustering algorithm to discover the categories of aspect terms. The clustering algorithm is able to find the semantic similarity of aspect terms, while considering the sharing words between aspect terms, and groups similar aspects in to a one cluster. After successfully generating an annotated corpus for the scholarly book reviews in the computer science domain with Cohen’s kappa statistics of 0.76, the dependency rule-based aspect extractor was able to extract both implicit and explicit aspects with precision 76.04%, recall 75.99% and overall F1-score 76.02%. The proposed semantic similarity based aspect clustering algorithm identifies the aspect in the following categories; book, author, readability, content, worthiness, price and author expertise with rand-index 14.41%, V-measure 36.29%, homogeneity 66.18% and completeness 25%
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
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
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
Automatic generation of elementary level mathematical question
No full textMathematical Word Problems (MWPs) play a vital role in mathematics education. An MWP is
a combination of not only the numerical quantities, units, and variables, but also textual content.
Therefore, in order to understand a particular MWP, a student requires knowledge in mathematics
as well as in literacy. This makes it difficult to solve MWPs when compared with other types of
mathematics problems. Therefore, students require a large number of similar questions to practice.
On the other hand, the composition of numerical quantities, units, and mathematical operations
impel the problems to possess specific constraints. Therefore, due to the inherent nature of MWPs,
tutors find it difficult to produce a lot of similar yet creative questions. Therefore, there is a timely
requirement of a platform that can automatically generate accurate and constraint-wise satisfied
MWPs.
Due to the template-based nature of existing approaches for automatically generating MWPs,
they tend to limit the creativity and novelty of the generated MWPs. Regarding the generation of
MWPs in multiple languages, language-specific morphological and syntactic features paves way
for extra constraints. Existing template-oriented techniques for MWP generation cannot identify
constraints that are language-dependant, especially in morphologically rich yet low resource languages
such as Sinhala and Tamil.
Utilizing deep neural language generation mechanisms, we deliver a solution for the aforementioned
restrictions. This thesis elaborates an approach by which a Long Short Term Memory
(LSTM) network which can generate simple MWPs while fulfilling above-mentioned constraints.
The methodology inputs a blend of character embeddings, word embeddings, and Part of Speech
(POS) tag embeddings to the LSTM network and the attention is produced for units and numerical
values. We used our model to generate MWPS in three languages, English, Sinhala, and Tamil. Irrespective
of the language, the model was capable of generating single and multi sentenced MWPs
with an average BLEU score of more than 20%
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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