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Named entity recognition (NER) is a vital task in spoken language understanding, which aims to identify mentions of named entities in text e.g., from transcribed speech.
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Z. Huang, W. Xu, and K. Yu, “Bidirectional LSTM-CRF models for sequence tagging,”
2015
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W. Radford, X. Carreras, and J. Henderson, “Named entity recognition with document-specific KB tag gazetteers,” in
2015
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C. N. dos Santos and V. Guimarães, “Boosting named entity recognition with neural character embeddings,” in
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V. Yadav and S. Bethard, “A survey on recent advances in named entity recognition from deep learning models,” in
2018
Closest in time.
E. F. T. K. Sang, “Introduction to the conll-2002 shared task: Language-independent named entity recognition,” in
2024
Closest in time.