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Keyword: named entity recognition ×Clear all

3 claims from 2 papers

1. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper compiles a biomedical NLP benchmark and reports that language models pretrained from scratch on biomedical text reach new state-of-the-art results across a wide range of tasks.

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   1. UncheckedFor fields with plenty of unlabelled text, like biomedicine, training language models from scratch gave substantial gains over adapting general-domain models.“In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”
   2. UncheckedWith BERT models, some common practices, such as complex tagging schemes for named entity recognition, are found to be unnecessary.“Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition.”

2. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper argues that biomedical language models pretrained from scratch on domain text outperform continually pretrained general models, and releases a benchmark (BLURB), models and a leaderboard.

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   1. UncheckedFor fields with plenty of unlabelled text, such as biomedicine, training a language model from scratch gives substantial gains over adapting a general-domain model.“In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”