C1F21 DIBBS: Porting Practical Natural Language Processing (NLP) and Machine Learning (ML) Semantics from Biomedicine to the Earth, Ice and Life Sciences

语义学是对基于单词的信息的研究。科学中充斥着以文字为基础的描述性数据：实地观察、材料和生境识别、参数名称和单位、事件和过程。语义在医学中也很重要，医学中必须描述人体和疾病。为了增强这些基于单词的(语义)系统之间的互操作性，并更容易地探索快速增长的大量语义数据，已经出现了一种以允许机器辅助的自动分析的方式组织基于单词的数据的运动。由于大量的资金投入，生物医学在组织和使用语义信息方面取得了很大的进展。该项目建立在生物医学领域的广泛投资的基础上，为迅速发展其他领域科学的语义概念组织提供了机会。由计算语言和教育研究中心(Clear TK)开发的工具包将用于构建三个科学领域(地质学、冰冻学和生物学)的语义资源(分类学、本体论和语义网络)。Clear Tk是一个最先进的自然语言处理(NLP)和机器学习(ML)系统，它还拥有用于机器辅助注释、验证、文档标记和事件提取的基本工具。Clear TK系统已被用于生物医学语义应用，包括在知名医院。在这个项目中，发展的重点是地质学、冰雪和生物学等科学领域。在这些领域中，需要从基于词的数据中准确地提取语义信息，以便用户能够快速地找到他们真正需要的数据。这个项目提供了一个宝贵的机会，与几个科学领域的专家一起扩展和评估语义能力。

Semantics is the study of word-based information. The sciences are filled with word-based descriptive data: field observations, materials and habitat identifications, parameter names and units, events and processes. Semantics are also important in medicine, where the human body and illnesses have to be described. To enhance interoperability among these word-based (semantic) systems, and to more readily explore the rapidly growing quantities of semantic data, there has been a movement towards organizing word-based data in ways that allow machine-assisted, automated analysis. Biomedicine has made great progress in organizing and using semantic information because of substantial funding investments. This project builds upon extensive investments in the biomedical field, providing an opportunity to rapidly develop the organization of semantic concepts for other domain sciences. A toolkit developed by the Center for Computational Language and Education Research (CLEAR TK) will be used to build semantic resources (taxonomies, ontologies, and semantic networks) for three science domains (geology, cryology, and biology). CLEAR TK is a state-of-the-art natural language processing (NLP) and machine learning (ML) system that also has essential tools for machine-assisted annotation, validation, document tagging, and event extraction. The CLEAR TK system has been used operationally for biomedical semantic applications, including in high-profile hospitals. In this project, developments are focused upon the science fields of geology, ice and snow, and biology. In these fields, accurate extraction of semantic information from the word-based data is required so users can quickly find the data they really need. This project provides a valuable opportunity to expand and evaluate semantic capabilities in conjunction with several scientific domain experts.