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Related Experiment Video

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Vector Representation of Complex Numbers
01:16

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A Method of Short Text Representation Based on the Feature Probability Embedded Vector.

Wanting Zhou1, Hanbin Wang1, Hongguang Sun2

  • 1School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.

Sensors (Basel, Switzerland)
|August 31, 2019
PubMed
Summary

This study introduces a new unsupervised method combining feature weighting, word embedding, and topic models for better text representation. The approach addresses limitations of traditional bag-of-words models in natural language processing.

Keywords:
feature weightinglatent Dirichlet allocationtext representationword embedding

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Information Retrieval

Background:

  • Traditional text representation methods like bag-of-words (BoW) suffer from semantic information loss, high dimensionality, and sparsity.
  • Deep learning methods offer potential solutions to overcome these limitations in NLP tasks.

Purpose of the Study:

  • To propose an unsupervised text representation method that integrates feature weighting, word embedding, and topic modeling.
  • To address the semantic information deficiency, high dimensionality, and sparsity issues inherent in the BoW model.

Main Methods:

  • Utilized Word2Vec for word embedding to generate word vectors.
  • Combined feature-weighted TF-IDF with the Latent Dirichlet Allocation (LDA) topic model.
  • Developed the feature, probability, and word embedding method for text representation.

Main Results:

  • The proposed method enhances the expressive capability of vector space models.
  • Successfully reduced the dimensionality of document vectors compared to traditional methods.
  • Demonstrated effectiveness in solving issues of insufficient information, high dimensions, and high sparsity of BoW.

Conclusions:

  • The feature, probability, and word embedding method offers a robust solution for text representation in NLP.
  • The method's validity was confirmed through its application in text categorization tasks.