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Related Experiment Video

Updated: Aug 23, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A supervised topic embedding model and its application.

Weiran Xu1, Koji Eguchi1

  • 1Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Hiroshima, Japan.

Plos One
|November 4, 2022
PubMed
Summary

We developed rTopicVec, a supervised topic embedding model, to predict document labels. This new method accurately predicts numerical labels for unlabeled documents, outperforming existing approaches.

Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • Topic modeling uncovers latent themes in documents using word co-occurrence.
  • Word embedding maps words to semantic spaces based on local context.
  • Topic embedding combines these by modeling topics within a word embedding space.

Purpose of the Study:

  • To introduce rTopicVec, a supervised topic embedding model.
  • To jointly model documents and their associated numerical labels using regression.
  • To enable prediction of response variables for unlabeled documents.

Main Methods:

  • Developed rTopicVec, a supervised topic embedding model incorporating regression.
  • Proposed a regularized variant of rTopicVec.

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  • Evaluated models on predicting stock returns from news and movie ratings from reviews.

    • Main Results:

    • rTopicVec achieved higher prediction accuracy than three baseline methods.
    • The performance improvement was statistically significant.
    • The model successfully predicted numerical labels for unlabeled documents.

    Conclusions:

    • rTopicVec offers an effective approach for supervised topic embedding.
    • The model demonstrates strong performance in regression tasks involving text data.
    • This method advances the prediction of document-associated variables.