1,721,002 research outputs found
Replication Data for: Exploring Gender Differences in Fatwa through Machine Learning
No full textReplication Data for: Exploring Gender Differences in Fatwa through Machine Learnin
Replication data for: Exploring Gender Differences in Fatwa through Machine Learning
No full textReplication data for the paper entitle Exploring Gender Differences in Fatwa through Machine Learnin
Replication Data for: Exploring Gender Differences in Fatwa through Machine Learning
No full textReplication Data for: Exploring Gender Differences in Fatwa through Machine Learnin
Author gender identification for Urdu articles
No full textThis is an accepted manuscript of an article published by Springer in Lecture Notes in Computer Science on 21/09/2022. The accepted version of the publication may differ from the final published versionIn recent years, author gender identification has gained considerable attention in the fields of computational linguistics and artificial intelligence. This task has been extensively investigated for resource-rich languages such as English and Spanish. However, researchers have not paid enough attention to perform this task for Urdu articles. Firstly, I created a new Urdu corpus to perform the author gender identification task. I then extracted two types of features from each article including the most frequent 600 multi-word expressions and the most frequent 300 words. After I completed the corpus creation and features
extraction processes, I performed the features concatenation process. As a result each article was represented in a 900D feature space. Finally, I applied 10 different well-known classifiers to these features to perform the author gender identification task and compared their performances against state-of-the-art pre-trained multilingual language models, such as mBERT, DistilBERT, XLM-RoBERTa and multilingual DeBERTa, as well as Convolutional Neural Networks (CNN). I conducted extensive experimental studies which show that (i) using the most frequent 600 multi-word expressions as features and concatenating them with the most frequent 300 words as features improves the accuracy of the author gender identification task, and (ii) support vector machines outperforms other classifiers, as well as fine-tuned pre-trained language models and CNN. The code base and the corpus can be found at: https://github.com/raheem23/Gender_Identification_Urdu
Author verification of Nahj Al-Balagha
Full text linkThis is an accepted manuscript of an article published by OUP in Digital Scholarship in the Humanities on 20/01/2022.
The accepted version of the publication may differ from the final published version. Available online at https://doi.org/10.1093/llc/fqab103The primary purpose of this paper is author verification of the Nahj Al-Balagha, a book attributed to Imam Ali and over which Sunni and Shi’i Muslims are proposing different theories. Given the morphologically complex nature of Arabic, we test whether morphological segmentation, applied to the book and works by the two authors suspected by Sunnis to have authored the texts, can be used for author verification of the Nahj Al-Balagha. Our findings indicate that morphological segmentation may lead to slightly better results than whole words, and that regardless of the feature sets, the three sub-corpora cluster into three distinct groups using Principal Component Analysis, Hierarchical Clustering, Multi-dimensional Scaling and Bootstrap Consensus Trees. Supervised classification methods such as Naive Bayes, Support Vector Machines, k Nearest Neighbours, Random Forests, AdaBoost, Bagging and Decision Trees confirm the same results, which is a clear indication that (a) the book is internally consistent and can thus be attributed to a single person, and (b) it was not authored by either of the suspected authors
Urdu AI: writeprints for Urdu authorship identification
Full text linkThis is an accepted manuscript of an article published by ACM in ACM Transactions on Asian and Low-Resource Language Information Processing on 31/10/2021, available online at: https://doi.org/10.1145/3476467
The accepted version of the publication may differ from the final published version.The authorship identification task aims at identifying the original author of an anonymous text sample from
a set of candidate authors. It has several application domains such as digital text forensics and information
retrieval. These application domains are not limited to a specific language. However, most of the authorship
identification studies are focused on English and limited attention has been paid to Urdu. On the other
hand, existing Urdu authorship identification solutions drop accuracy as the number of training samples per
candidate author reduces, and when the number of candidate author increases. Consequently, these solutions
are inapplicable to real-world cases. To overcome these limitations, we formulate a stylometric feature space.
Based on this feature space we use an authorship identification solution that transforms each text sample
into point set, retrieves candidate text samples, and relies the nearest neighbour classifier to predict the
original author of the anonymous text sample. To evaluate our method, we create a significantly larger corpus
than existing studies and conduct several experimental studies which show that our solution can overcome
the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous
authorship identification works
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
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