A transfer learning approach to cross-domain authorship attribution

Barlas, Georgios; Stamatatos, Efstathios

doi:10.1007/s12530-021-09377-2

Cited by 11 publications

(9 citation statements)

References 34 publications

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“…F1-score-a weighted harmonic mean of the Precision and Recall. As can be seen, the worst results over the three shuffles are obtained consistently by Emil Gârleanu (4), Mihai Oltean (6), and Liviu Rebreanu (8), while the best results are obtained by Mihai Eminescu (2) and Emilia Plugaru (7) and Petre Ispirescu (5).…”

Section: Discussionmentioning

confidence: 95%

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BERT-based Authorship Attribution on the Romanian Dataset called ROST

Sanda-Maria¹

2023

Preprint

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Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT.Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87% macro-accuracy.

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Section: Discussionmentioning

confidence: 95%

“…The adoption of using deep neural learning, which was already used for Natural Language Processing (NLP), occurred later for authorship identification. More recently, pre-trained language models (such as BERT and GPT-2) have been used for finetuning and accuracy improvements [2,4,5].…”

Section: Introductionmentioning

confidence: 99%

BERT-based Authorship Attribution on the Romanian Dataset called ROST

Sanda-Maria¹

2023

Preprint

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“…As a particular example, it is important to know what this technology has the very promising ability to bridge the gap between generic and patient specific computational models. An instant classic is the example of ChatGPT’s ability to ‘learn’ a language, and then tailor sentences to the style of writing of a given author [ 63 ], which is a form of transfer learning from generic to particular. Such approach is already being applied to, for example, training a transformer on numerous examples of brain structure to then detect a patient-specific anomaly (i.e., a tumor) in spite of inter-subject variability [ 64 ].…”

Section: Modeling For Personalizationmentioning

confidence: 99%

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Valero-Cuevas,

Finley,

Orsborn

et al. 2024

J NeuroEngineering Rehabil

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We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation’s Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling ‘In-the-Wild’. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

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“…However, the drawback of n-grams is that they produce sparse features as the number of n increases [47]. Traditional feature representations, such as Tf-Idf, suffers from data sparsity and high dimensionality in representing n-grams and have difficulty in grasping the semantic meaning of texts [15].…”

Section: Reviews Of Ai Work In Smfmentioning

confidence: 99%

Towards Curbing Cyber-Bullying in Malaysia by Author Identification of Iban and Kadazandusun Osn Text Using Deep Learning

Tarmizi

Saee

Ibrahim

2023

AEJ

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Online Social Network (OSN) is frequently used to carry out cyber-criminal actions such as cyberbullying. As a developing country in Asia that keeps abreast of ICT advancement, Malaysia is no exception when it comes to cyberbullying. Author Identification (AI) task plays a vital role in social media forensic investigation (SMF) to unveil the genuine identity of the offender by analysing the text written in OSN by the candidate culprits. Several challenges in AI dealing with OSN text, including limited text length and informal language full of internet jargon and grammatical errors that further impact AI's performance in SMF. The traditional AI system that analyses long text documents seems inadequate to analyse short OSN text's writing style. N-gram features are proven to efficiently represent the authors' writing style for shot text. However, representing N-grams in traditional representation like Tf-IDF resulted in sparse and difficult in grasping the semantic information from text. Besides, most AI works have been done in English but receive less attention in indigenous languages. In West Malaysia, the supreme languages that transcend ethnic boundaries are Iban of Sarawak and KadazanDusun of Sabah, which both are inherently under-resourced. This paper presented a proposed workflow of AI for short OSN text using two Under-Resourced Language (U-RL), Iban and KadazanDusun tweets, to curb the cyberbullying issue in Malaysia. This paper compares Tf-Idf (sparse) and SoA embedding-based (dense) feature representations to observe which representations best represent the stylistic features of the authors’ writing. N-grams of word, character, and POS were extracted as the features. The representation models were learned by different classifiers using machine learning (Naïve Bayes, Random Forest, and SVM). The convolutional neural network (CNN), a SoA deep learning model in sentence classification, was tested against the traditional classifiers. The result was observed by combining different representation models and classifiers on three datasets (English, Iban, and KadazanDusun). The best result was achieved when CNN learned embedding-based models with a combination of all features. KadazanDusun achieved the highest accuracy with 95.76%, English with 95.02%, and Iban with 94%..

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A transfer learning approach to cross-domain authorship attribution

Cited by 11 publications

References 34 publications

BERT-based Authorship Attribution on the Romanian Dataset called ROST

BERT-based Authorship Attribution on the Romanian Dataset called ROST

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Towards Curbing Cyber-Bullying in Malaysia by Author Identification of Iban and Kadazandusun Osn Text Using Deep Learning

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