ITR: Automated Structuring of Text Information

目前，对大规模文本集合中的信息的访问在很大程度上限于基于关键字的搜索，其检索整个文档或段落。 虽然这些工具在检索一般主题的信息时通常令人满意，但它们对访问涉及特定关系、事件或事实的信息提供的支持很少。信息提取技术提供了从大型文本集合中创建选定关系的结构化表格表示的可能性，这些表示可以支持更详细的文档查询。 然而，到目前为止，为广泛的关系开发提取系统过于昂贵和耗时，无法考虑以这种方式使用。 本研究计划将：（1）利用语料库技术自动识别子语言中最常见的关系（关于特定主题的一套文本），以及这些关系在文本中表达的不同方式; 2）构建系统以从新文本中提取关于这些关系的信息，构建表格摘要;以及3）提供用于查询这些关系和访问底层文档的用户界面。 总之，这些工具应该提供重要的新功能，用于访问大型文本集合中的信息。

At present, access to the information in large-scale text collections is largely limited to keyword-based searches which retrieve entire documents or passages. While such tools are often satisfactory in retrieving information on general topics, they provide little support for accessing information involving specific relationships, events, or facts.Information extraction technology offers the possibility of creating structured, tabular representations of selected relations from large text collections --- representations which can support more detailed document querying. Until now, however, developing extraction systems for a broad range of relations has been too expensive and time-consuming to consider its use in this way. Recent developments in extraction system customization offer the promise of substantially easing this task, and so making this approach to document indexing feasible.This research project will: 1) use corpus-based techniques to automatically identify the most common relationships within a sublanguage (the set of texts concerning a particular subject matter), and the different ways in which these relations are expressed in the text; 2)construct systems to extract information about these relationships from new text, building tabular summaries; and 3) provide a user interface for querying these relationships and accessing the underlying documents. Taken together, these tools should offer significant new capabilities for accessing the information in large text collections.