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Abstract. This paper explores the application of restricted relationship graphs (RDF) and statistical NLP techniques to improve named entity annotation in challenging Informal English domains. We validate our approach using on-line forums discussing popular music. Named entity annotation is particularly difficult in this domain because it is characterized by a large number of ambiguous entities, such as the Madonna album "Music" or Lilly Allen's pop hit "Smile". We evaluate improvements in annotation accuracy that can be obtained by restricting the set of possible entities using real-world constraints. We find that constrained domain entity extraction raises the annotation accuracy significantly, making an infeasible task practical. We then show that we can further improve annotation accuracy by over 50% by applying SVM based NLP systems trained on word-usages in this domain.

In this paper, we empirically investigate the performance effect of team-specific human capital in highly interactive teams. Based on the tenets of the resource-based view of the firm and on the ideas of typical learning functions, we hypothesize that team members' shared experience in working together positively impacts team performance, but at diminishing rates. Holding a team's stock of general human capital and other potential drivers constant, we find support for this prediction. Implications concerning investment decisions into human capital as well as the transferability of our findings to other contexts are discussed.