III: Small: Collaborative Research: Keyphrase Extraction in Document Networks

文档的关键短语使用一小组短语(即，文档中的连续单词序列)来简明地描述文档。例如，关键短语“社交网络”和“兴趣定向”迅速为我们提供了文档的高级主题描述(即，摘要)，该文档集中于在社交网络上下文中向用户推荐诸如产品和新闻之类的服务的兴趣定向。考虑到当今非常庞大的文档集合，这些关键短语不仅对于概述文档，而且对于搜索和检索相关信息都是极其重要的。然而，关键短语并不总是直接可用的。相反，它们需要从文件中的许多细节中收集。该项目解决了从研究论文中自动提取关键短语的问题，这是共享和传播科学发现的促进因素。该项目的目标是探索准确的方法，利用文档网络自动发现和提取文档中的关键短语，这将帮助用户在这些“大数据”时代以更短的时间处理和消化更多的信息。在教育方面，本研究将包括培养研究生和本科生在关键短语提取活跃的研究领域，这在许多现实世界的应用中都有很高的影响，如在线广告、文档分类、推荐和摘要、Web搜索和发现以及新闻专线中的话题跟踪。虽然到目前为止已经做了很多关于关键词自动提取的研究，但以前的方法还没有通过连接网络中的文档的引用关系来捕捉文档相互之间的影响。这个项目将调查考虑到文献网络中引用文献和被引用文献之间的联系的模型，并将探索这个问题的各种定性和定量方面：“文献中的关键短语或概念是什么？”将设计和开发可扩展的迭代算法，以捕获文档的不同方面(例如，主题或概念)，以及文档网络中一个文档对另一个文档的影响(例如，影响或主题演变)。这项研究的结果将直接传递到CiteSeerX数字图书馆(http://citeseerx.ist.psu.edu).在本项目过程中开发的软件、工具和基准数据集将通过项目网站(http://people.cs.ksu.edu/~ccaragea/keyphrases.html).广泛传播所有研究成果将通过学术期刊上的出版物分享给研究界，并在信息检索、文本挖掘和自然语言处理会议上展示。

Keyphrases for a document concisely describe the document using a small set of phrases (i.e., sequences of contiguous words in a document). For example, the keyphrases "social networks" and "interest targeting" quickly provide us with a high-level topic description (i.e., a summary) of a document focused on targeting interest for recommending services such as products and news to users, in the context of social networks. Given today's very large collections of documents, these keyphrases are extremely important not only for summarizing a document, but also for the search and retrieval of relevant information. However, keyphrases are not always available directly. Instead, they need to be gleaned from the many details in documents. This project addresses the problem of automatic keyphrase extraction from research papers, which are enablers of the sharing and dissemination of scientific discoveries. The goal of the project is to explore accurate approaches that automatically discover and extract keyphrases in documents, using document networks, which will help users handle and digest more information in less time during these "big data" times. Educationally, this research will involve training of both graduate and undergraduate students in the active area of research of keyphrase extraction, which has high impact in many real-world applications such as online advertising, document categorization, recommendation, and summarization, Web search and discovery, and topic tracking in newswire. Although much research to date has been done on automatic keyphrase extraction, no previous approaches have captured the impact of documents on one another via the citation relation that connects documents in a network. This project will investigate models that take into consideration the linkage between citing and cited documents in a document network and will explore various qualitative and quantitative aspects of the question: "What are the key phrases or concepts in a document?" Scalable iterative algorithms will be designed and developed that capture different aspects of documents (e.g., topics or concepts), as well as the impact of one document on another (e.g., influence or topic evolution) in a document network. The results of this research will have a direct pipeline to the CiteSeerX digital library (http://citeseerx.ist.psu.edu). The software, tools, and benchmark datasets developed during the course of this project will be broadly disseminated via the project website (http://people.cs.ksu.edu/~ccaragea/keyphrases.html). All findings will be shared to the research community through publications in academic journals and presented in Information Retrieval, Text Mining and Natural Language Processing conferences.