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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Impact analysis of keyword extraction using contextual word embedding.

Muhammad Qasim Khan1, Abdul Shahid1, M Irfan Uddin1

  • 1Institute of Computing, Kohat University of Science & Technology, Kohat, Kohat, Pakistan.

Peerj. Computer Science
|June 20, 2022
PubMed
Summary

Context-based keyword extraction significantly outperforms traditional methods. The KeyBERT model, utilizing contextual word embeddings, achieved higher accuracy in identifying key terms from document abstracts.

Keywords:
Contextual Word EmbeddingKeyword extractionTF-IDFText RankYake

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Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Computational Linguistics

Background:

  • Keywords are crucial for document understanding, indexing, and retrieval.
  • Traditional keyword extraction relies heavily on statistical term frequencies.
  • Contextual information is increasingly recognized as vital for semantic understanding.

Purpose of the Study:

  • To validate the significance of context-based keyword extraction over traditional statistical methods.
  • To evaluate the performance of the proposed KeyBERT methodology for keyword extraction.
  • To compare the KeyBERT approach against established algorithms like Text Rank, Rake, Gensim, Yake, and TF-IDF.

Main Methods:

  • Utilizing contextual word embeddings for keyword identification.
  • Applying the KeyBERT model to extract keywords from document abstracts.
  • Comparing extracted keywords against author-assigned keywords for similarity assessment.

Main Results:

  • The KeyBERT model demonstrated superior performance compared to traditional keyword extraction techniques.
  • Context-based keyword extraction proved significant in reflecting the core ideas of a document.
  • The proposed KeyBERT approach achieved an average similarity of 51% with author-assigned keywords.

Conclusions:

  • Contextual word embeddings enhance keyword extraction accuracy.
  • The KeyBERT model offers a more effective approach to identifying salient keywords than traditional methods.
  • This research highlights the importance of semantic context in automated keyword generation for improved information retrieval.