Name tagging is a key component of Information Extraction (IE), particularly
in scientific domains such as biomedicine and chemistry, where large language
models (LLMs), e.g., ChatGPT, fall short. We investigate the applicability of
transfer learning for enhancing a name tagging model trained in the biomedical
domain (the source domain) to be used in the chemical domain (the target
domain). A common practice for training such a model in a few-shot learning
setting is to pretrain the model on the labeled source data, and then, to
finetune it on a hand-full of labeled target examples. In our experiments we
observed that such a model is prone to mis-labeling the source entities, which
can often appear in the text, as the target entities. To alleviate this
problem, we propose a model to transfer the knowledge from the source domain to
the target domain, however, at the same time, to project the source entities
and target entities into separate regions of the feature space. This diminishes
the risk of mis-labeling the source entities as the target entities. Our model
consists of two stages: 1) entity grouping in the source domain, which
incorporates knowledge from annotated events to establish relations between
entities, and 2) entity discrimination in the target domain, which relies on
pseudo labeling and contrastive learning to enhance discrimination between the
entities in the two domains. We carry out our extensive experiments across
three source and three target datasets, and demonstrate that our method
outperforms the baselines, in some scenarios by 5\% absolute value.

我们研究了在生物医学领域与化学领域之间使用迁移学习提升实体标注模型的适用性，通过将源域和目标域的实体映射到不同的特征空间，我们的模型在源域进行实体分组并建立实体之间的关系，然后在目标领域进行实体鉴别，通过大量实验证明我们的方法优于基准模型，有时提高了 5% 的绝对值效果。

通过度量学习在生命科学中处理领域转移的命名标记

Name Tagging Under Domain Shift via Metric Learning for Life Sciences

Name tagging in low-resource languages or domains suffers from inadequate
training data. Existing work heavily relies on additional information, while
leaving those noisy annotations unexplored that extensively exist on the web.
In this paper, we propose a novel neural model for name tagging solely based on
weakly labeled (WL) data, so that it can be applied in any low-resource
settings. To take the best advantage of all WL sentences, we split them into
high-quality and noisy portions for two modules, respectively: (1) a
classification module focusing on the large portion of noisy data can
efficiently and robustly pretrain the tag classifier by capturing textual
context semantics; and (2) a costly sequence labeling module focusing on
high-quality data utilizes Partial-CRFs with non-entity sampling to achieve
global optimum. Two modules are combined via shared parameters. Extensive
experiments involving five low-resource languages and fine-grained food domain
demonstrate our superior performance (6% and 7.8% F1 gains on average) as well
as efficiency.

该研究提出了一种基于弱标注数据的名字标注神经模型，通过使用 Partial-CRFs 等技术，有效地解决了低资源语言或领域中不足的培训数据问题，并在五种低资源语言和细粒度的食品领域上表现出显著的性能和效率改善。