Using Graphical Approaches for Entity Reconciliation: Attaining Locally and Globally Consistent Entity Annotations
نویسنده
چکیده
Graphical models provide a very convenient way for representing entity sequences in several problems related to information retrieval, data mining etc. Inferencing in such entity sequences involves making use of not only local information(that is labels depending upon a small window around the entity giving rise to simpler models like chains, trees etc) but also global information(that is labels depending upon the ”far-off” entities giving rise to complicated graphs). Inferencing on simpler graphs like chains and trees can be done exactly and tractably. On the contrary, in case of complicated graphs, exact inferencing is computationally expensive and often intractable. We review the importance of the global information and several methods to incorporate it during inferencing. We also demonstrate inferencing involving one such combination of local and global layer by combining CRF and Belief Propagation.
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