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Entity-Based Short Text Classification Using Convolutional Neural Networks

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12387)

Abstract

It is beyond human capabilities to analyze a huge amount of short text produced on the World Wide Web in the form of search queries, social media platforms, etc. Due to many difficulties underlying short text for automated processing, i.e, sparsity and insufficient context, the traditional text classification approaches cannot easily be applied to short text. This study discusses a Convolutional Neural Network (CNN) based approach for short text classification. Given a short text, the model generates the text representation by leveraging words together with the entities. To validate the effectiveness of the model, several experiments have been conducted on different datasets. The results suggest that the proposed model is capable of performing short text classification with a high accuracy and outperforms the baseline.

Keywords

  • Short text classification
  • Convolutional Neural Networks
  • Text classification

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Notes

  1. 1.

    https://tagme.d4science.org/tagme/.

  2. 2.

    https://www.cs.york.ac.uk/semeval-2013/task2/.

  3. 3.

    http://www.cs.cornell.edu/people/pabo/movie-review-data/.

  4. 4.

    http://cogcomp.cs.illinois.edu/Data/QA/QC/.

  5. 5.

    http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html.

  6. 6.

    https://wikipedia2vec.github.io/wikipedia2vec/pretrained/.

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Correspondence to Mehwish Alam .

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Alam, M., Bie, Q., Türker, R., Sack, H. (2020). Entity-Based Short Text Classification Using Convolutional Neural Networks. In: Keet, C.M., Dumontier, M. (eds) Knowledge Engineering and Knowledge Management. EKAW 2020. Lecture Notes in Computer Science(), vol 12387. Springer, Cham. https://doi.org/10.1007/978-3-030-61244-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-61244-3_9

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  • Publisher Name: Springer, Cham

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