tailieunhanh - Báo cáo khoa học: "Graph-based Semi-Supervised Learning Algorithms for NLP"

While labeled data is expensive to prepare, ever increasing amounts of unlabeled linguistic data are becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. | Graph-based Semi-Supervised Learning Algorithms for NLP Amar Subramanya Google Research asubram@ Partha Pratim Talukdar Carnegie Mellon University ppt@ Abstract While labeled data is expensive to prepare ever increasing amounts of unlabeled linguistic data are becoming widely available. In order to adapt to this phenomenon several semi-supervised learning SSL algorithms which learn from labeled as well as unlabeled data have been developed. In a separate line of work researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms which bring together these two lines of work have been shown to outperform the state-of-the-art in many applications in speech processing computer vision and NLP. In particular recent NLP research has successfully used graph-based SSL algorithms for PoS tagging Subramanya et al. 2010 semantic parsing Das and Smith 2011 knowledge acquisition Talukdar et al. 2008 sentiment analysis Goldberg and Zhu 2006 and text categorization Subramanya and Bilmes 2008 . Recognizing this promising and emerging area of research this tutorial focuses on graph-based SSL algorithms . label propagation methods . The tutorial is intended to be a sequel to the ACL 2008 SSL tutorial focusing exclusively on graph-based SSL methods and recent advances in this area which were beyond the scope of the previous tutorial. The tutorial is divided in two parts. In the first part we will motivate the need for graph-based SSL methods introduce some standard graph-based SSL algorithms and discuss connections between these approaches. We will also discuss how linguistic data can be encoded as graphs and show how graph-based algorithms can be scaled to large amounts of data . web-scale data . Part 2 of the tutorial will focus on how graph-based methods can be used to solve several critical NLP tasks including basic problems such as PoS tagging semantic parsing and more .