近年來，自然語言處理在生物醫學的專業領域上遇見障礙；專業領域的語言使用和一般領域大相逕庭。在基因本體學等非常專精的領域中常常缺乏大型的訓練資料集，使得強大的深度學習技巧難以在小資料集中施展。
我們在研究中所採用的 Colorado Richly Annotated Full-Text 資料集包含 67 篇的全文文件，由生物學家標註「基因本體學」的資料。我們在研究中找尋出「基因本體學概念辨識」的困難所在，並且用「有名字的概念」為刀，把難題一分為二，分別用字典查找和機器學習來克服。第一步我們先用「有名字的概念」把「基因本體學概念」的資料重新架構，第二步我們再運重新架構的「基因本體學」來完成概念辨識的需求。
我們的系統在 F1-measure 上比先前頂尖的系統進步了約 20%，達到 0.804 的 precision 及 0.715 的 recall。我們也證明了使用「有名字的概念」的想法有效，或許可以推廣到其他專業的語言上。

In recent years, natural language processing has been facing several obstacles in professional text mining in biomedical fields; the scenarios of natural language processing usage are completely different for handling professional languages and general languages. Due to the lack of training data, powerful deep learning techniques were not applicable to small datasets available for the highly specific biological researches such as gene ontology.
The Colorado Richly Annotated Full-Text corpus used in this study contains 67 full-text documents annotated by biologists. In this research, we aimed to identify the key difficulty of the gene ontology concept recognition task and handled this problem using dictionary-matching and machine-learning techniques. Accordingly, problem solving was divided into two steps, dictionary-matching and machine-learning respectively, corresponding to the roles of named concepts. In the first step, we reconstructed the gene ontology concepts after mining the named concepts. Furthermore, in the second step, we leveraged this reconstructed data to fulfill the needs of the proposed hybrid method.
The proposed concept recognizer achieved approximately 20% improvement in F1-measure as compared to the state-of-the-art system resulting in 0.804 precision and 0.715 recall. It proved that the named concept may be applied to the concept recognition of other professional languages.

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