Automatic identification of relevant chemical compounds from patents. The training corpus.

Background In commercial research and development projects, public disclosure of new chemical compounds often takes place in patents. Only a small proportion of these compounds are published in journals, usually a few years after the patent. Patent authorities make available the patents but do not provide systematic continuous chemical annotations. Content databases such as Elsevier's Reaxys provide such services mostly based on manual excerptions, which are time-consuming and costly. Automatic text-mining approaches help overcome some of the limitations of the manual process. Different text-mining approaches exist to extract chemical entities from patents. Majority of them have been developed using sub-sections of patent documents and focus on mentions of compounds. Less attention has been given to relevancy of a compound in a patent. Relevancy of a compound to a patent is based on the patent's context. A relevant compound plays a major role within a patent. Identification of relevant compounds reduces the size of the extracted data and improves the usefulness of patent resources (e.g., supports identifying the main compounds). Annotators of databases like Reaxys only annotate relevant compounds. In this study, we design an automated system that extracts chemical entities from patents and classifies their relevance. To develop and evaluate the system, a patent corpus with annotations for chemical entities and their relevance was constructed.研究背景：在商业研发项目中，新化合物的公开披露通常见于专利文献。其中仅有极小部分化合物会在专利授权数年后发表于学术期刊。专利主管部门仅公开专利文本，但未提供系统化的持续性化学标注服务。诸如爱思唯尔（Elsevier）的Reaxys这类内容数据库，虽可提供此类标注服务，但大多依赖人工摘录，不仅耗时且成本高昂。自动化文本挖掘方法可弥补人工流程的部分局限。目前已有多种面向专利文本的化学实体提取文本挖掘方法，其中多数基于专利文档的特定章节开发，且仅关注化合物的提及情况，但现有方法较少关注化合物在专利中的相关性判定。化合物与专利的相关性需结合专利上下文判定，相关化合物在专利中占据核心地位。识别相关化合物可缩减提取数据的规模，提升专利资源的利用价值（例如助力核心化合物的筛选）。诸如Reaxys这类数据库的标注人员仅会标注相关化合物。本研究设计了一套自动化系统，可从专利文本中提取化学实体并对其相关性进行分类。为开发并评估该系统，我们构建了一份包含化学实体及其相关性标注的专利语料库。