Analysis of Clinical Text: Task 14 of SemEval 2015

SemEval (Semantic Evaluation) is an ongoing series of evaluations of computational semantic analysis systems, organized under the umbrella of SIGLEX, the Special Interest Group on the Lexicon of the Association for Computational Linguistics. This project describes "Analysis of Clinical Text" task of the International Workshop on Semantic Evaluation 2014 and 2015 (SemEval 2014 and 2015) [2,3,4,5]. The purpose of the task is to enhance current research in natural language processing (NLP) methods used in the clinical domain, and to introduce clinical text processing to the broader NLP community. The task aims to combine supervised methods for text analysis with unsupervised approaches for entity/acronym/abbreviation recognition and mapping to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). It also evaluated systems on the task of template filling [1], which involves the population of eight attributes of the identified disorders with their normalized values.语义评测（SemEval，Semantic Evaluation）是一项持续性的计算语义分析系统评测系列活动，由国际计算语言学协会（Association for Computational Linguistics, ACL）旗下的词汇学特别兴趣小组SIGLEX（Special Interest Group on the Lexicon）统筹主办。本项目介绍了2014年与2015年国际语义评测研讨会（SemEval 2014、SemEval 2015）的“临床文本分析”任务[2,3,4,5]。该任务的核心目标在于推动临床领域自然语言处理（Natural Language Processing, NLP）相关方法的现有研究进展，并将临床文本处理技术推广至更广泛的NLP社区。任务旨在将监督式文本分析方法，与用于实体、首字母缩略词及缩写识别并映射至统一医学语言系统（Unified Medical Language System, UMLS）概念唯一标识符（Concept Unique Identifiers, CUIs）的无监督方案相结合。此外，本次评测还针对模板填充任务[1]对系统性能进行评估，该任务要求为识别出的病症填充八项属性及其标准化取值。