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Is word segmentation necessary for Vietnamese sentiment classification?

arXiv 2301.00418 ↗First posted 1 Jan 2023cs.CLcited by 0

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Abstract (arXiv)

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

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References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  3. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  5. 2017Decoupled Weight Decay Regularizationcited by 1,831
  6. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  7. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  8. 2018Adaptive Input Representations for Neural Language Modelingcited by 226
  9. 2020PhoBERT: Pre-trained language models for Vietnamesecited by 149
  10. 2019Emotion Recognition for Vietnamese Social Media Textcited by 23
  11. 2017A Fast and Accurate Vietnamese Word Segmentercited by 21
  12. 2021SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligencecited by 12
8 more
  1. 2017From Word Segmentation to POS Tagging for Vietnamesecited by 6
  2. 2020Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviewscited by 6
  3. 2018A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsingcited by 5
  4. 2017BKTreebank: Building a Vietnamese Dependency Treebankcited by 4
  5. 2020Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capturecited by 3
  6. 2017Vietnamese Semantic Role Labellingcited by 2
  7. 2021Span Labeling Approach for Vietnamese and Chinese Word Segmentationcited by 2
  8. 2019State-of-the-Art Vietnamese Word Segmentationcited by 1

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