Search Engines Support for HLT Applications

该项目扩展了最初为结构化（例如，XML）文档，为问答、计算机辅助语言学习和其他人类语言技术（HLT）应用程序所需的功能提供方便的支持。 之所以需要这些新功能，是因为HLT应用程序使用搜索引擎在大型文本数据库中查找信息变得越来越普遍。 HLT应用通常使用大量的文本注释来减少便于文本检索的基于词的表示和便于推理的基于概念的表示之间的不匹配。 他们也可以描述检索文本段落必须满足的非常具体的要求。 这种文本搜索的使用与特别的交互式搜索有很大的不同，目前的搜索引擎并不能很好地支持它。 该项目扩展了概率索引和检索模型，以支持多个，详细的，重叠的文本注释，分层文本注释，注释与相关的confidencevalues，以及类似的功能。 通过明确承认HLT应用程序作为一流的用户，拟议的研究拓宽了研究界的文本搜索远远超出了简单的查询，文本表示和检索模型，其特点是交互式的，特设的搜索今天的看法。 它还可以通过提供一种媒介来改善交互式搜索，在这种媒介中，搜索界面可以表达来自简化用户和任务模型的约束和偏好。 研究结果在技术论文中传播，并作为开源Lemur Toolkit的一部分。 该项目的网站（http：//www.cs.cmu.edu/callan/Projects/IIS-0534345/）提供了更多的信息。

This project extends search engine indexing and retrieval modelsdeveloped initially for structured (e.g., XML) documents to provideconvenient support for capabilities required by question answering,computer-assisted language learning, and other human languagetechnology (HLT) applications. These new capabilities are requiredbecause it is becoming common for HLT applications to use searchengines to find information in large text databases. HLT applicationstypically use extensive text annotations to reduce the mismatchbetween the word-based representations convenient for text retrievaland the concept-based representations convenient for reasoning. Theyalso can describe very specific requirements that retrieved textpassages must satisfy. This use of text search is very different thanad-hoc, interactive search, and current search engines do not supportit well. This project extends probabilistic indexing and retrievalmodels to support multiple, detailed, overlapping text annotations,hierarchical text annotations, annotations with associated confidencevalues, and similar capabilities. By explicitly recognizing HLTapplications as first-class users, the proposed research broadens theresearch community's view of text search well beyond the simplequeries, text representations, and retrieval models that characterizeinteractive, ad-hoc search today. It may also lead to improvedinteractive search by providing a medium in which search interfacescan express constraints and preferences derived from sophisticateduser and task models. Research results are disseminated in technicalpapers, and as part of the open-source Lemur Toolkit. The project Website (http://www.cs.cmu.edu/~callan/Projects/IIS-0534345/) providesadditional information.