Exploiting Semantic Analysis of Documents

利用文档语义分析

• 批准号: RGPIN-2015-06183
• 负责人: Milios, Evangelos
• 金额: $ 3.13万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688628
• 关键词: Exploiting; Semantic; Analysis; Documents

Consider the tasks of organizing a collection of research papers for the purpose of writing a thesis; organizing the set of accepted papers at a conference into meaningful and coherent sessions; looking up a corpus of incident reports in customer service to locate the most relevant cases and their resolution to the new case on hand; or discovering novel treatments for diseases through implicit connections in the biomedical literature.  ***A core problem underlying such tasks is that of semantic relatedness of documents. Semantic relatedness of documents should not be limited to the sharing of words, as two documents may be about the same topic, but using different vocabulary (for example a medical document for experts versus a medical document for the layperson). Given a domain-specific corpus, topic models have been fit to documents and terms, leading to the representation of documents as instances of generative probabilistic models of mixtures of topics. Topic models require corpora and documents of sufficient size to be robust. In real life, documents may be short (e.g. titles or abstracts) and document corpora may contain a small number of documents (tens or hundreds instead of thousands), rendering topic models unreliable. The proposed research program will investigate semantic relatedness measures that are applicable to any domain and rely on readily available external knowledge sources, such as the Google n-gram corpus and Wikipedia. ****Organizing document collections into semantically coherent clusters has typically relied on bag-of-word document representations, with a focus more on mathematical sophistication than the interpretability of the document representation by the user. In the proposed research program we will seek algorithms and processes that support the human user in her sense making process, providing support to her in interactively steering the document representation and clustering process to fit her objectives. ***In collaboration with industrial partners, we will test the proposed methods in different applications of practical significance, such as interactive clustering of corporate document sets, automatic ranking of resumes against job ads, expertise mapping and matchmaking, paper referee assignment, and content-based recommendation of news to digital newspaper subscribers. A long term objective is to support document-based discovery in the majority of scientific fields that lack the sophistication of terminological and ontological resources currently available in the biomedical field. **

考虑为撰写论文而组织一系列研究论文的任务；将会议上被接受的一组论文组织成有意义和连贯的会议；查阅客户服务部门的事件报告，找出最相关的个案，并找出解决方法；或者通过生物医学文献中的隐性联系发现新的疾病治疗方法。这些任务背后的核心问题是文档的语义相关性。文档的语义相关性不应局限于单词的共享，因为两个文档可能涉及相同的主题，但使用不同的词汇（例如，专家的医疗文档与外行人的医疗文档）。给定特定领域的语料库，主题模型已经适合于文档和术语，导致文档作为主题混合生成概率模型的实例表示。主题模型需要足够大小的语料库和文档来实现健壮性。在现实生活中，文档可能很短（例如标题或摘要），文档语料库可能包含少量文档（数十或数百而不是数千），这使得主题模型不可靠。提出的研究计划将研究适用于任何领域的语义相关性度量，并依赖于现成的外部知识来源，如b谷歌n-gram语料库和维基百科。****将文档集合组织到语义上一致的集群中通常依赖于word包文档表示，它更多地关注数学复杂性，而不是用户对文档表示的可解释性。在拟议的研究计划中，我们将寻求支持人类用户的意义生成过程的算法和过程，为她提供支持，以交互式地指导文档表示和聚类过程，以适应她的目标。***我们将与行业伙伴合作，在不同具有实际意义的应用中测试所提出的方法，如企业文档集的交互式聚类、简历与招聘广告的自动排序、专业知识映射和匹配、论文推荐人分配、以及向数字报纸订阅者推荐基于内容的新闻。一个长期目标是在大多数科学领域支持基于文献的发现，这些领域缺乏目前在生物医学领域可用的复杂的术语和本体论资源。**