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ITR: Automated Structuring of Text Information

ITR：文本信息的自动构建

基本信息

批准号：
0081962
负责人：
Ralph Grishman
金额：
$ 49.98万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2000
资助国家：
美国
起止时间：
2000-09-01 至 2004-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0081962&HistoricalAwards=false
关键词：
ITR Automated Structuring Text Information

项目摘要

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.

目前，对大规模文本集合中信息的访问很大程度上局限于基于关键字的搜索，即检索整个文档或段落。虽然这些工具在检索关于一般主题的信息方面通常令人满意，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——这种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它。提取系统定制方面的最新发展有望大大简化这一任务，从而使这种文档索引方法变得可行。该研究项目将：1)使用基于语料库的技术自动识别子语言（关于特定主题的文本集）中最常见的关系，以及这些关系在文本中表达的不同方式；2)构建系统，从新文本中提取有关这些关系的信息，构建表格摘要；3)提供查询这些关系和访问底层文档的用户界面。总之，这些工具应该为访问大型文本集合中的信息提供重要的新功能。