Exploiting Semantic Analysis of Documents

考虑以下任务：为了写论文而组织研究论文集;将会议上接受的论文集组织成有意义和连贯的会议;查找客户服务中的事件报告语料库，以找到最相关的案例及其对手头新案例的解决方案;或者通过生物医学文献中的隐含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仅限于单词的共享，因为两个文档可能是关于同一主题的，但使用不同的词汇（例如，专家的医学文档与外行的医学文档）。给定一个特定领域的语料库，主题模型已经适合于文档和术语，导致将文档表示为主题混合物的生成概率模型的实例。主题模型需要足够大的语料库和文档才能保持健壮。在真实的生活中，文档可能很短（例如标题或摘要），并且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。拟议的研究计划将调查适用于任何领域的语义相关性措施，并依赖于现成的外部知识来源，如谷歌n-gram语料库和维基百科。* 将文档集合组织成语义一致的集群通常依赖于词袋文档表示，更注重数学复杂性，而不是用户对文档表示的可解释性。在拟议的研究计划中，我们将寻求算法和流程，支持人类用户在她的意义决策过程中，提供支持，她在交互式地引导文档表示和聚类过程，以适应她的目标。* 在与行业合作伙伴的合作中，我们将在具有实际意义的不同应用中测试所提出的方法，例如公司文档集的交互式聚类，简历对招聘广告的自动排名，专业知识映射和匹配，纸质裁判分配，以及基于内容的新闻推荐给数字报纸订阅者。一个长期的目标是支持大多数科学领域的基于文档的发现，这些领域缺乏生物医学领域目前可用的术语和本体资源的复杂性。**

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. **