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• W2963563735 abstract "Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers." @default.
• W2963563735 created "2019-07-30" @default.
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• W2963563735 date "2017-01-01" @default.
• W2963563735 modified "2023-10-12" @default.
• W2963563735 title "Semi-supervised sequence tagging with bidirectional language models" @default.
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• W2963563735 doi "https://doi.org/10.18653/v1/p17-1161" @default.
• W2963563735 hasPublicationYear "2017" @default.
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