UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 8
August-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2308195


Registration ID:
523010

Page Number

b783-b785

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Title

Entity Recognition and Key Phrase Extraction in A Scientific Paper with Relationship Detection Using sci-BERT and miniLM sentence transformer: A Review

Abstract

This paper illustrated a novel approach for enhancing the automated analysis of scientific papers through the combined use of two powerful natural language processing (NLP) models: sci-BERT and mini-LM sentence transformer. Our methodology aims to extract critical information from scientific documents, including entity recognition, key phrase extraction, and relationship detection. First, we fine-tune the sci-BERT model, which is pre-trained on a large corpus of scientific literature, to perform entity recognition. By leveraging the rich context-specific embeddings of sci-BERT, we achieve improved accuracy in identifying and categorizing various entities mentioned in the scientific texts. Next, we employ the mini-LM sentence transformer to extract key phrases that encapsulate the main themes and concepts discussed within the papers. The mini-LM model's capability to capture semantic meaning from sentences enhances the effectiveness of key phrase extraction, facilitating better understanding and summarization of the content. Furthermore, we introduce a relationship detection component, which enables the identification of connections between entities mentioned in the scientific papers. By exploiting the embeddings from both sci-BERT and mini-LM, we establish relationships between entities and reveal valuable insights into the interconnections and dependencies present within the research. To evaluate the performance of our proposed approach, we use a diverse dataset of scientific papers from multiple domains. Our experimental results demonstrate significant improvements in entity recognition and key phrase extraction when compared to traditional NLP techniques. Additionally, the relationship detection component proves effective in identifying meaningful associations among entities. Overall, this research contributes to advancing the state-of-the-art in automated scientific paper analysis. The integration of sci-BERT and miniLM sentence transformer offers a robust and comprehensive solution for effectively extracting critical information from scientific texts, enabling researchers, scholars, and scientists to efficiently process and comprehend the wealth of knowledge present in the vast scientific literature.

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"Entity Recognition and Key Phrase Extraction in A Scientific Paper with Relationship Detection Using sci-BERT and miniLM sentence transformer: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.b783-b785, August-2023, Available :http://www.jetir.org/papers/JETIR2308195.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Entity Recognition and Key Phrase Extraction in A Scientific Paper with Relationship Detection Using sci-BERT and miniLM sentence transformer: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppb783-b785, August-2023, Available at : http://www.jetir.org/papers/JETIR2308195.pdf

Publication Details

Published Paper ID: JETIR2308195
Registration ID: 523010
Published In: Volume 10 | Issue 8 | Year August-2023
DOI (Digital Object Identifier):
Page No: b783-b785
Country: Ranchi, Ranchi, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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