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Document similarity analysis is critical for various NLP tasks like information retrieval and plagiarism detection. Traditional methods based on word-to-word mapping struggle with capturing contextual nuances. Existing solutions lack the capability to provide domain-specific accuracy and enriched search experiences. One such field is finding similar research papers. Often researchers struggle to find papers similar to a certain paper and have to rely on basic keyword-based search. This hinders to provide the best match based on the overall context. In this work, we propose a novel methodology that integrates BERT with a Siamese neural network to capture semantic textual similarity of research papers. Our approach goes beyond simple similarity evaluation by conducting a nuanced semantic search of overall context and provides a representative similarity score. This offers a more accurate and refined search experience. Furthermore, we curate a dataset of over 10,000 NLP research paper abstracts to train our model. The model excels in identifying the contextual relationships between documents, making it highly effective for domain-specific applications. This model can significantly improve the user experience in document retrieval systems, particularly for academic research and recommendation.

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An Approach for Evaluating Semantic Similarity in Research Papers via Siamese BERT Architecture

  • 会议论文

  • 发表日期：

    2026年

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  • Pritam Sarkar,
  • Soumyaneel Sarkar,
  • Wazib Ansar,
  • Amlan Chakrabarti

摘要

Document similarity analysis is critical for various NLP tasks like information retrieval and plagiarism detection. Traditional methods based on word-to-word mapping struggle with capturing contextual nuances. Existing solutions lack the capability to provide domain-specific accuracy and enriched search experiences. One such field is finding similar research papers. Often researchers struggle to find papers similar to a certain paper and have to rely on basic keyword-based search. This hinders to provide the best match based on the overall context. In this work, we propose a novel methodology that integrates BERT with a Siamese neural network to capture semantic textual similarity of research papers. Our approach goes beyond simple similarity evaluation by conducting a nuanced semantic search of overall context and provides a representative similarity score. This offers a more accurate and refined search experience. Furthermore, we curate a dataset of over 10,000 NLP research paper abstracts to train our model. The model excels in identifying the contextual relationships between documents, making it highly effective for domain-specific applications. This model can significantly improve the user experience in document retrieval systems, particularly for academic research and recommendation.