CLJan 28, 2023
Semantic Tagging with LSTM-CRF
arXiv:2301.12206v11 citationsh-index: 1
Originality Synthesis-oriented
AI Analysis
This is an incremental study comparing model variants for semantic tagging, relevant to NLP researchers working on sequence labeling tasks.
The paper tackles semantic tagging by comparing LSTM-CRF and BERT-LSTM-CRF models on a universal semantic tag dataset, finding that LSTM-CRF converges more easily while BERT-LSTM-CRF requires more data and time for effectiveness.
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.