@inproceedings{Yeh:2009:WRW:1708124.1708133,
  abstract = {Computing semantic relatedness of natural language texts is a key component of tasks such as information retrieval and summarization, and often depends on knowledge of a broad range of real-world concepts and relationships. We address this knowledge integration issue by computing semantic relatedness using personalized PageRank (random walks) on a graph derived from Wikipedia. This paper evaluates methods for building the graph, including link selection strategies, and two methods for representing input texts as distributions over the graph nodes: one based on a dictionary lookup, the other based on Explicit Semantic Analysis. We evaluate our techniques on standard word relatedness and text similarity datasets, finding that they capture similarity information complementary to existing Wikipedia-based relatedness measures, resulting in small improvements on a state-of-the-art measure.},
  acmid = {1708133},
  address = {Stroudsburg, PA, USA},
  author = {Yeh, Eric and Ramage, Daniel and Manning, Christopher D. and Agirre, Eneko and Soroa, Aitor},
  booktitle = {Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing},
  interhash = {8b28cd800b6ad3929eef3b45de997e51},
  intrahash = {ffd20a7357ca8e87d46e516589a7769e},
  isbn = {978-1-932432-54-1},
  location = {Suntec, Singapore},
  numpages = {9},
  pages = {41--49},
  publisher = {Association for Computational Linguistics},
  series = {TextGraphs-4},
  title = {WikiWalk: random walks on Wikipedia for semantic relatedness},
  url = {http://dl.acm.org/citation.cfm?id=1708124.1708133},
  year = 2009
}

@misc{backstrom2010supervised,
  abstract = {Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open.   We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function.   Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.},
  author = {Backstrom, L. and Leskovec, J.},
  interhash = {970b02221d407c64c1c35f997d4fe345},
  intrahash = {c5cc52fa016b384f9d7b5ae4da841d44},
  note = {cite arxiv:1011.4071},
  title = {Supervised Random Walks: Predicting and Recommending Links in Social   Networks},
  url = {http://arxiv.org/abs/1011.4071},
  year = 2010
}

@inproceedings{kluegl2012stacked,
  abstract = {Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.},
  address = {Vilamoura, Algarve, Portugal},
  author = {Klügl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank},
  booktitle = {Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM},
  editor = {Carmona, Pedro Latorre and Sánchez, J. Salvador and Fred, Ana},
  interhash = {74969e59c5637d192021e35bbd02bece},
  intrahash = {7920d13d4fce68bb9a4947585083986e},
  pages = {240-248},
  publisher = {SciTePress},
  title = {Stacked Conditional Random Fields Exploiting Structural Consistencies},
  url = {http://ki.informatik.uni-wuerzburg.de/papers/pkluegl/2012-ICPRAM-StackedCRF.pdf},
  year = 2012
}

@misc{Sutton2010,
  abstract = {  Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields. },
  author = {Sutton, Charles and McCallum, Andrew},
  interhash = {05e1b6859124c5bf51c7aafd63f779b0},
  intrahash = {49d8c9beb76a8b88739aa9eece7446ee},
  note = {cite arxiv:1011.4088Comment: 90 pages},
  title = {An Introduction to Conditional Random Fields},
  url = {http://arxiv.org/abs/1011.4088},
  year = 2010
}