Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT

Keskeiset käsitteet

Authorship attribution in Bangla literature is enhanced through the use of transfer learning and the AWD-LSTM architecture, addressing complex linguistic features and scalability issues.

Tiivistelmä

The paper discusses the importance of authorship attribution in identifying original authors, especially with increased anonymity online. It highlights the lack of research in Bangla literature due to its linguistic complexity. The proposed model utilizes transfer learning and AWD-LSTM architecture to achieve superior performance. The study introduces a new dataset, BAAD16, for authorship attribution tasks in Bangla literature.
The content emphasizes the challenges faced in authorship attribution due to language complexity and limited datasets. It proposes a novel approach using transfer learning and AWD-LSTM architecture to address these challenges effectively. The study showcases the effectiveness of different tokenization methods and introduces a new dataset for evaluation purposes.
Furthermore, it discusses the significance of authorship attribution across various fields like security, plagiarism detection, and criminal law. The paper outlines the limitations of existing systems and presents a detailed methodology for training language models using pre-trained datasets. Overall, the study aims to advance research in authorship attribution within Bangla literature through innovative techniques.

Tilastot

Despite significant advancements in other languages such as English, Spanish, and Chinese, Bangla lacks comprehensive research.
Proposed model achieved 99.8% accuracy in BAAD16 dataset.
Publicly available dataset contains 17,966 sample texts and 13.4+ million words.
Existing systems are not scalable with increasing number of authors.
Most previous works use small datasets with a maximum of 10 authors.

Lainaukset

"Anonymity is widespread due to internet usage; authorship attribution is crucial."
"Proposed model outperformed state-of-the-art models with 99.8% accuracy."
"Bangla lacks significant work due to its high inflection and complex structure."

Tärkeimmät oivallukset

Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT

by Aisha Khatun... klo arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05519.pdf

Syvällisempiä Kysymyksiä

How can authorship attribution impact cybersecurity measures beyond plagiarism detection?

Authorship attribution plays a crucial role in enhancing cybersecurity measures beyond just plagiarism detection. One significant impact is in the field of forensic investigation, where identifying the true authors of malicious or fraudulent content becomes essential. By analyzing writing styles and linguistic patterns, authorship attribution can help trace back cybercrimes to their perpetrators, aiding law enforcement agencies in solving cases related to hacking, online fraud, or cyberbullying.
Moreover, in the realm of threat intelligence and cybersecurity operations, authorship attribution can assist in attributing specific threats or attacks to known threat actors or hacker groups. Understanding the writing style and behavioral patterns of these malicious entities can provide valuable insights for developing targeted defense strategies and proactive security measures.
Additionally, authorship attribution can be utilized in email spoofing detection and phishing scams. By analyzing the language used in suspicious emails or messages, organizations can identify potential impersonation attempts and prevent data breaches or financial losses resulting from social engineering attacks.
Overall, authorship attribution serves as a powerful tool for enhancing cybersecurity by enabling accurate identification of individuals behind digital content and activities that pose risks to information security.

How might advancements in NLP benefit other areas within Bangla literature beyond authorship attribution?

Advancements in Natural Language Processing (NLP) offer numerous benefits for various areas within Bangla literature beyond authorship attribution:

Machine Translation: Improved NLP models enable more accurate translation between languages including Bangla. This advancement facilitates cross-cultural communication through automated translation services for literary works, academic research papers, and cultural exchanges.

Sentiment Analysis: NLP techniques allow sentiment analysis on Bangla text to understand emotions expressed by authors or readers. This capability is beneficial for gauging public opinion on literary works, identifying trends in reader preferences, and improving marketing strategies for publishers.

Text Summarization: Advanced NLP algorithms support automatic summarization of lengthy texts into concise versions without losing critical information. In Bangla literature, this technology aids researchers by providing quick overviews of complex texts while preserving key details.

Named Entity Recognition (NER): NER tools powered by NLP help identify names of people...

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What potential biases or limitations could arise from using transfer learning for authorship attribution?

While transfer learning offers significant advantages for authorship attribution tasks...
However,...