Graph-based Approaches to Text Processing

基于图的方法为一系列数据密集型应用程序形成了强大的框架：任何涉及成对关系（如相似性）的数据集都可以自然地表示为底层对象上的图，并且以图形式构建自然语言和相关数据使这些数据能够被强大的组合算法访问。至关重要的是，高效的图算法支持以全局方式分析数据关系，这大大增加了可以从带注释和未注释的数据中提取的信息量。这项工作开发了基于图论思想的新算法，包括网络流和频谱分析。它将这些方法应用于文本和语言处理的一系列不同应用领域，包括基于情感的文本分类和文档检索；并将这些问题与当前社会网络分析中的一些问题联系起来。这项工作的主要结果将是为自然语言处理社区提供一套新的有效的，理论上合理的基于图的范式，用于从语言数据中学习。此外，这项工作将通过传播新的数据集和通过教育活动为更广泛的研究界提供资源。

Graph-based methods form a powerful framework for a range of data-intensive applications: any dataset involving pairwise relationships such as similarities can be naturally expressed as a graph on the underlying objects, and structuring natural-language and related data in graph form makes this data accessible to powerful combinatorial algorithms. Crucially, efficient graph algorithms enable the analysis of data relationships in a global fashion, which greatly increases the amount of information that can be distilled from both annotated and un-annotated data.This work develops new algorithms based on graph-theoretic ideas, including network flow and spectral analysis. It applies these methods to a range of different application areas in text and language processing, including sentiment-based text categorization and document retrieval; it also develops connections between these problems and some current issues in social network analysis. The main results of this work will be to open up to the natural language processing community a new set of effective, theoretically sound graph-based paradigms for learning from language data. In addition, this work will make resources available to the broader research community through the dissemination of novel datasets and through educational activities.