Problem Of Author Identification Research Articles

PROBLEMS OF BIBLIOGRAPHICAL MANAGERS FOR SCIENCE AUTOMATIZATION: APPROACH TO SOLVE AND ONTOLOGICAL VEIWPOINT

This paper explores the functionalities and limitations of major reference manager systems, focusing on their role in solving the author identification problem in academic research. It critically analyzes the features of popular reference managers like Zotero, EndNote, Mendeley, SciWheel, and Paperpile, assessing their capabilities in reference management and their approaches to author identification. The study reveals that while these systems offer robust reference management functionalities, including citation generation, bibliography management, and collaborative tools, they fall short in integrating advanced author identification mechanisms, such as ORCID. The lack of such features highlights a significant gap in current reference management solutions, impacting the accuracy and efficiency of scholarly communication. The paper emphasizes the need for enhanced features in reference managers to address author identification challenges effectively, particularly in the context of Open Science and FAIR data management principles. This study contributes to the understanding of current limitations in reference managers and underscores the importance of developing advanced features for accurate author attribution in the digital era of academic research. It focuses on using additional integrational data about the authors by using ORCID as the main identifier. This solution is crucial for countries with non-Latin alphabet, including Cyrillic.

Machine learning-based authorship attribution using token n-grams and other time tested features

Authorship Attribution is a process to determine and/or identify the author of a given text document. The relevance of this research area comes to the fore when two or more writers claim to be the prospective authors of an unidentified or anonymous text document or are unwilling to accept any authorship. This research work aims to utilize various Machine Learning techniques in order to solve the problem of author identification. In the proposed approach, a number of textual features such as Token n-grams, Stylometric features, bag-of-words and TF-IDF have been extracted. Experimentation has been performed on three datasets viz. Spooky Author Identification dataset, Reuter_50_50 dataset and Manual dataset with 3 different train-test split ratios viz. 80-20, 70-30 and 66.67-33.33. Models have been built and tested with supervised learning algorithms such as Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree and Random Forest. The proposed system yields promising results. For the Spooky dataset, the best accuracy score obtained is 84.14% with bag-of-words using Naïve Bayes classifier. The best accuracy score of 86.2% is computed for the Reuter_50_50 dataset with 2100 most frequent words when the classifier used is Support Vector Machine. For the Manual dataset, the best score of 96.67% is obtained using the Naïve Bayes Classification Model with both 5-fold and 10-fold cross validation when both syntactic features and 600 most frequent unigrams are used in combination.

THE IMPACT OF TEXT REPRESENTATION AND PREPROCESSING ON AUTHOR IDENTIFICATION

Author identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification.