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Semantic Tagging with LSTM-CRF

arXiv 2301.12206 ↗First posted 28 Jan 2023cs.CLcited by 0

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Abstract (arXiv)

In the present paper, two models are presented namely LSTM-CRF and BERT-LSTM-CRF for semantic tagging of universal semantic tag dataset. The experiments show that the first model is much easier to converge while the second model that leverages BERT embedding, takes a long time to converge and needs a big dataset for semtagging to be effective.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 16 references that are in the map. First posted on arXiv 28 Jan 2023.

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13 of 16 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2016Neural Architectures for Named Entity Recognitioncited by 548
  4. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
  5. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  6. 2018Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Taskscited by 56
  7. 2020Compositional Generalization via Semantic Taggingcited by 32
  8. 2015Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representationcited by 28
  9. 2019Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERTcited by 19
  10. 2017Towards Universal Semantic Taggingcited by 12
  11. 2016Semantic Tagging with Deep Residual Networkscited by 4
  12. 2018What can we learn from Semantic Tagging?cited by 2
1 more
  1. 2023Semantic Operator Prediction and Applicationscited by 1

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