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

该项目将通过生物医学领域的英语和德语功能原型，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。在此之前，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的基础上，并结合各种跨语言信息检索方法。生物医学中大量被广泛接受的本体论资源（例如MeSH和UMLS）的存在使一种新的语言间方法成为可能，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。本研究将比较现有技术（基于词典和基于语料库）与新的语言间方法在各种评估维度上的差异，如I点平均精度、计算可追溯性和端到端用户可接受性。为了判断后者，将开发一个完整的原型系统；这是该项目的欧洲方面的主要焦点。除了开发和评估这些新的CLIR方法，并产生和评估可用的原型应用程序之外，该项目还将提供CLIR之外的其他好处，通过基于语料库的自动词义消歧来适当提高红外精度；开发用于创建多语言词汇和短语资源的统计方法；使用最大边际相关性方法提供检索文档的自动按需摘要；改进生物医学领域的多语言信息访问和管理系统。

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.