Visual text analytics for total recall information retrieval in large noisy text datasets

用于大型噪声文本数据集中的总召回信息检索的视觉文本分析

• 批准号: 499941-2016
• 负责人: Milios, Evangelos
• 金额: $ 9.57万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618847
• 关键词: Visual; text; analytics; total; recall

The search paradigm for finding information of interest in massive text corpora is well established, and exemplified by the success of Web search engines. The next frontier is the support of sense-making out of medium to large scale text corpora by the domain expert and analyst, who is trying to untap the tacit knowledge hidden in the text. In contrast to Web search, where the user's information need is satisfied by a few high-quality results, the domain analyst typically needs to find all relevant documents related to a topic of interest. Furthermore the domain analyst's information need is often much too complex to be easy to express in a small number of query terms. These requirements are very different from those of a web search engine. This project aims towards algorithms and systems that will address such requirements, in the context of use cases of significant practical interest including: reports on factory worker incidents and on aviation incidents; court decisions in the common law legal system; medical research literature for generation of systematic reviews; and prior art patent search. Key challenges include: vocabulary mismatch, where the queries of the user contain a vocabulary that is different from that used in the relevant documents; design of interactive mechanisms and visualizations to support the interactive nature of and incorporate human feedback into the retrieval process. The impact of the proposed research will be improvement of safety in a variety of domains where incidents are recorded and studied to obtain insights on how to prevent future incidents, such as in factories, aviation, hospitals and old-age homes.

在海量文本语料库中查找感兴趣信息的搜索范式已经建立，并以Web搜索引擎的成功为例。下一个前沿是支持领域专家和分析师从中到大规模的文本语料库中提取意义，他们试图挖掘隐藏在文本中的隐性知识。与Web搜索相比，用户的信息需求由一些高质量的结果来满足，领域分析师通常需要找到与感兴趣的主题相关的所有相关文档。此外，领域分析师的信息需求往往过于复杂，很难用少量的查询词来表达。这些要求与Web搜索引擎的要求非常不同。该项目的目标是在具有重大实际意义的用例的背景下解决这些要求的算法和系统，包括：工厂工人事故和航空事故报告;普通法法律的体系中的法院判决;用于生成系统评价的医学研究文献;以及现有技术专利检索。主要挑战包括：词汇不匹配，用户的查询包含与相关文档中使用的词汇不同的词汇;设计交互机制和可视化，以支持检索过程的交互性，并将人类反馈纳入检索过程。拟议研究的影响将是改善各种领域的安全性，记录和研究事故，以了解如何防止未来的事故，如工厂，航空，医院和养老院。