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

文档的关键短语使用一小组短语（即文档中连续单词的序列）简明地描述文档。例如，关键词“社交网络”和“兴趣定位”可以快速为我们提供一个高层次的文档主题描述（即摘要），该文档专注于在社交网络背景下，针对用户推荐产品和新闻等服务的兴趣定位。考虑到今天非常大的文档集合，这些关键短语不仅对于总结文档非常重要，而且对于搜索和检索相关信息也非常重要。然而，关键字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。这个项目解决了从研究论文中自动提取关键词的问题，这是科学发现的共享和传播的推手。该项目的目标是探索使用文档网络自动发现和提取文档中的关键短语的准确方法，这将帮助用户在“大数据”时代以更短的时间处理和消化更多信息。在教育方面，本研究将包括对研究生和本科生进行关键字提取研究的培训，关键字提取在许多现实世界的应用中具有很高的影响，例如在线广告，文档分类，推荐和摘要，网络搜索和发现以及新闻中心的主题跟踪。尽管迄今为止在自动关键字提取方面已经做了很多研究，但以前没有任何方法可以通过网络中连接文档的引文关系来捕获文档之间的影响。本项目将研究考虑文献网络中被引文献和被引文献之间联系的模型，并将探索“文献中的关键短语或概念是什么？”这一问题的各种定性和定量方面。将设计和开发可扩展的迭代算法，以捕获文档网络中文档的不同方面（例如，主题或概念），以及一个文档对另一个文档的影响（例如，影响或主题演变）。这项研究的结果将有一个直接的管道到CiteSeerX数字图书馆（http://citeseerx.ist.psu.edu）。在此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。所有的研究成果将在学术期刊上发表，并在信息检索、文本挖掘和自然语言处理会议上发表。

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.