MLIAM: MUCHMORE: Multilingual Concept Hierarchies for Medical Information Organization and Retrieval

该项目将通过生物医学领域英语和德语的功能原型，在基础科学和技术实现方面扩展高性能多语言信息访问的最新技术水平。因此，跨语言信息检索（CLIR）是建立在基于词典的查询翻译方法或基于语料库的词汇映射统计学习，结合各种IR方法。在生物医学中存在大量的、被广泛接受的本体论资源（例如，MeSH和UMLS）实现了一种新的语际方法，其中查询和文档都自动映射到多个分类类别中，允许直接的概念匹配。本研究将比较现有的技术（基于词典和语料库）与新的语际方法的各种评价维度，如I-I点的平均精度，计算的易处理性，和端到端用户的可接受性。为了判断后者，将开发一个完整的原型系统;这是该项目欧洲方面的主要重点。除了开发和评估这些新的CLIR方法，并制作和评估可用的原型应用程序外，该项目还将提供CLIR本身以外的其他好处：通过基于语料库的自动词义消歧提高IR精度;开发用于创建多语言词汇和短语资源的统计方法;使用最大边缘相关性方法提供检索到的文档的自动按需摘要;完善生物医学领域多语种信息获取和管理系统。

This project will extend the state of the art in high performance multilingual information access, both in terms of underlying science and its technological realization via a functional prototype for English and German in the biomedical domain. Heretofore, Cross-Lingual Information Retrieval (CLIR) was founded upon dictionary-based query translation methods or corpus-based statistical learning of vocabulary mappings, combined with various IR methods. The existence of large, well accepted ontological resources in biomedicine (e.g., MeSH and UMLS) enables a new interlingual approach wherein both queries and documents are mapped into multiple taxonomic categories automatically, permitting direct conceptual matching. This research will compare existing techniques (dictionarybased and corpus-based) with the new interlingual methods on various evaluative dimensions, such as I I-point average precision, computational tractability, and end-to-end user acceptability. To judge the latter, a full prototype system will be developed; that is the main focus of the European side of the project. In addition to developing and evaluating these new CLIR methods, and producing and evaluating a usable prototype application, this project will provide other benefits beyond CLIR proper improving IR precision via automated corpus-based word sense disambiguation; developing statistical methods for the creation of multilingual lexical and phrasal resources; providing automated on-demand summarization of retrieved documents using the Maximal Marginal Relevance method; and improving multilingual information access and management systems forthe biomedical domain.