Biomedical Natural Language Processing (NLP) tends to become cumbersome for
most researchers, frequently due to the amount and heterogeneity of text to be
processed. To address this challenge, the industry is continuously developing
highly efficient tools and creating more flexible engineering solutions. This
work presents the integration between industry data engineering solutions for
efficient data processing and academic systems developed for Named Entity
Recognition (LasigeUnicage\_NER) and Relation Extraction (BiOnt). Our design
reflects an integration of those components with external knowledge in the form
of additional training data from other datasets and biomedical ontologies. We
used this pipeline in the 2022 LitCoin NLP Challenge, where our team
LasigeUnicage was awarded the 7th Prize out of approximately 200 participating
teams, reflecting a successful collaboration between the academia (LASIGE) and
the industry (Unicage). The software supporting this work is available at
https://github.com/lasigeBioTM/Litcoin-Lasige_Unicage.

本研究介绍了将工业数据工程解决方案与生物医学命名实体识别和关系抽取系统相结合，通过引入来自其他数据集和生物医学本体知识的附加训练数据，以解决生物医学自然语言处理中的文本处理问题。我们在 2022 年 LitCoin NLP 挑战中使用了这一流程，并与大约 200 个参赛团队合作，最终获得了第七名的奖项，彰显了学术界（LASIGE）与工业界（Unicage）之间成功的合作。