Albanian Authorship Attribution Model

Misini, Arta; Kadriu, Arbana; Canhasi, Ercan

doi:10.1109/meco58584.2023.10155046

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…While many studies rely on established benchmark datasets like Enron [20], C50 [7], PAN [22], IMDb62 [6,21] and others [9], the scarcity of standard datasets, particularly for low-resource languages, presents a unique challenge. Creating specialized corpora has paved the way for promising advancements in the field, demonstrated by projects like UNAAC [5], BAAD [2], UrduCorpus [5], A3C Corpus [8,25], and more [4]. These corpora tailored for AA contribute significantly to the field, expanding its resources.…”

Section: Related Workmentioning

confidence: 99%

“…These corpora tailored for AA contribute significantly to the field, expanding its resources. AA research encompasses a diverse array of data, ranging from literary works [2][3][4], news articles [5,8,23,24], tweets [9,19], and blog posts [21]. Albanian, being resource-constrained, has garnered limited attention in NLP.…”

Section: Related Workmentioning

confidence: 99%

“…The goal was to develop a high-quality dataset for subsequent authorship analysis within Albanian literature. Our previous research, as detailed in [8,25], investigated the utilization of newsroom columns for AA.…”

Section: Aaalitcorpusmentioning

confidence: 99%

“…In today's digital age [1], the Internet has expanded anonymous content, making AA an increasingly concern. This issue carries substantial implications across various domains, including literature [2][3][4], journalism [5][6][7][8], and forensics [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Authorship classification techniques: Bridging textual domains and languages

Misini,

Kadriu,

Canhasi

2024

IJITS JOURNAL

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Authorship classification analyzes an author's prior work to identify their writing style, a unique trait of each language and individual author. This research aims to conduct a thorough comparative analysis of various methods for classifying authorship. The study leverages two corpora: AAALitCorpus of Albanian literary texts and CCAT10 of English columns. We evaluate model-generated features across different configurations. The richness of the features and the breadth of the analysis provide a significant understanding of the problem, setting a new standard for comprehensive linguistic investigations across multiple languages. The study indicates that machine learning algorithms accurately discern authorial writing styles, highlighting the complexities of classifying authorship in a cross-linguistic context.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Aaalitcorpusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Authorship classification techniques: Bridging textual domains and languages

Misini,

Kadriu,

Canhasi

2024

IJITS JOURNAL

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Sentiment Analysis Using Machine Learning Techniques on IMDB Dataset

Başa,

Basarslan

2023

2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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Automatic authorship attribution in Albanian texts

Misini,

Canhasi,

Kadriu

et al. 2024

PLoS ONE

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Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (lexical, morphological, and structural) relevant to Albanian and experiment with multiple classifiers using two different multiclass classification strategies. Furthermore, we compare our results to those obtained using deep learning models. Our investigation focuses on identifying the best combination of features and classification methods. The results reveal that lexical features are the most effective set of linguistic features, significantly improving the performance of various algorithms in the authorship attribution task. Among the machine learning algorithms evaluated, XGBoost demonstrated the best overall performance, achieving an F1 score of 0.982 on literary works and 0.905 on newsroom columns. Additionally, deep learning models such as fastText and BERT-multilingual showed promising results, highlighting their potential applicability in specific scenarios in Albanian writings. These findings contribute to the understanding of effective methods for authorship attribution in low-resource languages and provide a robust framework for future research in this area. The careful analysis of the different scenarios and the conclusions drawn from the results provide valuable insights into the potential and limitations of the methods and highlight the challenges in detecting authorship in Albanian. Promising results are reported, with implications for improving the methods used in Albanian authorship attribution. This study provides a valuable resource for future research and a reference for researchers in this domain.

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Albanian Authorship Attribution Model

Cited by 3 publications

References 26 publications

Authorship classification techniques: Bridging textual domains and languages

Authorship classification techniques: Bridging textual domains and languages

Sentiment Analysis Using Machine Learning Techniques on IMDB Dataset

Automatic authorship attribution in Albanian texts

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