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A multi-view representation technique based on principal component analysis for enhanced short text clustering.

Majid Hameed Ahmed1,2, Sabrina Tiun1, Nazlia Omar1

  • 1CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

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Clustering short texts is challenging due to limited information. This study introduces multi-view representation (MVR) by combining various single-view methods, significantly improving clustering performance.

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Area of Science:

  • Data Mining
  • Information Retrieval
  • Natural Language Processing

Background:

  • Clustering texts is crucial for data mining and information retrieval, aiming to group unlabeled data into meaningful sets.
  • Clustering short texts (STC) presents unique challenges due to sparsity, ambiguity, and noise in the data.
  • Existing STC methods often rely on single-view text representations, which are insufficient for capturing diverse textual aspects.

Purpose of the Study:

  • To enhance short text clustering (STC) by proposing and evaluating a multi-view representation (MVR) approach.
  • To identify the optimal combination of single-view representations for effective MVR in STC.
  • To investigate the impact of different MVR strategies on the quality of text clusters.

Main Methods:

  • Developed a multi-view representation (MVR) strategy by combining various single-view text representations.
  • Utilized Principal Component Analysis (PCA) for fixed-length concatenation of single-view representations to create MVRs.
  • Evaluated different MVR combinations on three standard datasets: Twitter, Google News, and StackOverflow.

Main Results:

  • Experimental results demonstrated that multi-view representation (MVR) significantly improves short text clustering (STC) performance compared to single-view methods.
  • The most effective MVR for STC was a 5-view combination, integrating BERT, GPT, TF-IDF, FastText, and GloVe representations.
  • The study highlights the necessity of carefully selecting single-view representations for optimal MVR design.

Conclusions:

  • Multi-view representation (MVR) is a superior approach for enhancing short text clustering (STC).
  • The effectiveness of MVR hinges on the judicious selection and combination of diverse single-view text representations.
  • Future research should focus on advanced methods for constructing optimal MVRs for STC.