Biomedical queries have become increasingly prevalent in web searches,
reflecting the growing interest in accessing biomedical literature. Despite
recent research on large-language models (LLMs) motivated by endeavours to
attain generalized intelligence, their efficacy in replacing task and
domain-specific natural language understanding approaches remains questionable.
In this paper, we address this question by conducting a comprehensive empirical
evaluation of intent detection and named entity recognition (NER) tasks from
biomedical text. We show that Supervised Fine Tuned approaches are still
relevant and more effective than general-purpose LLMs. Biomedical transformer
models such as PubMedBERT can surpass ChatGPT on NER task with only 5
supervised examples.

通过全面的实证评估，我们表明在生物医学文本中，受监督的微调方法仍然相关且比通用性的大型语言模型更有效，如 PubMedBERT 可以仅凭 5 个受监督示例就能在命名实体识别任务上超过 ChatGPT。

从生物医学文献中进行意图识别和实体提取

Intent Detection and Entity Extraction from BioMedical Literature

We introduce novel mathematical models and algorithms to generate (shortest
or k different) explanations for biomedical queries, using answer set
programming. We implement these algorithms and integrate them in BIOQUERY-ASP.
We illustrate the usefulness of these methods with some complex biomedical
queries related to drug discovery, over the biomedical knowledge resources
PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY and ORPHADATA. To
appear in Theory and Practice of Logic Programming (TPLP).

本研究采用 ANSWER SET PROGRAMMING（ASP）来生成医学查询的解释，包括最短或 k 个不同的解释，我们实现了这些算法并将其集成到 BIOQUERY-ASP 中，使用多种医学知识资源展示了这些方法的实用性，包括 PHARMGKB、DRUGBANK、BIOGRID、CTD、SIDER、DISEASE ONTOLOGY 和 ORPHADATA 等。