mlsanityData to 14 Oct 2026

Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

arXiv 1510.06168 ↗First posted 21 Oct 2015cs.CLcited by 9

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Abstract (arXiv)

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation for characterizing the statistical properties of natural language. In this study, we propose to use BLSTM-RNN with word embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is achieved. Without using morphological features, this approach can also achieve a good performance comparable with the Stanford POS tagger.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2015.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 2.

9 papers.

  1. 2023Pushing the Limits of ChatGPT on NLP Taskscited by 27
  2. 2021Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Modelcited by 26
  3. 2021Conformal prediction for text infilling and part-of-speech predictioncited by 22
  4. 2021Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Densitycited by 3
  5. 2022CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Textscited by 2
  6. 2022Effective Transfer Learning for Low-Resource Natural Language Understandingcited by 1
  7. 2024Extracting chemical food safety hazards from the scientific literature automatically using large language modelscited by 1
  8. 2023Generation Of Colors using Bidirectional Long Short Term Memory Networkscited by 0
  9. 2024GWPT: A Green Word-Embedding-based POS Taggercited by 0