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We begin by introducing the Computer Science branch of Natural LanguageProcessing, then narrowing the attention on its subbranch of InformationExtraction and particularly on Named Entity Recognition, discussing briefly itsmain methodological approaches. It follows an introduction to state-of-the-artConditional Random Fields under the form of linear chains. Subsequently, theidea of constrained inference as a way to model long-distance relationships ina text is presented, based on an Integer Linear Programming representation ofthe problem. Adding such relationships to the problem as automatically inferredlogical formulas, translatable into linear conditions, we propose to solve theresulting more complex problem with the aid of Lagrangian relaxation, of whichsome technical details are explained. Lastly, we give some experimentalresults.

Named entity recognition using conditional random fields with non-local relational constraints