cubbie | Stupid simple state storage | Storage library

It's probably best to think of your problem in two parts: role labelling (Named Entity Recognition) and label unification (fuzzy matching).

For determining labels - that is, marking tokens in the sentences as team name, person, and so on - a Conditional Random Field (CRF) is a good model. CRF++ is a popular toolkit. The New York Times used CRF++ with some success on recipe data a few years back. Here's a bit from their article:

Since you're identifying the names of sports teams, you have two options for dealing with the fuzzy matching you described. You can do actual fuzzy matching using string similarity; this article explains how that was done in Python library Fuzzy Wuzzy at a high enough level it should be easy to re-implement.

Your other option is Named Entity Resolution, which is tying named entities (your labelled bits) to an external database. When you do this with Wikipedia it's called "Wikification", for example. This article describes someone using Wikipedia redirect information to recognize alternate names for companies - you could to the same thing by checking that Wikipedia redirects Cubbies to Chicago Cubs (it does).

Without knowing your data, it's hard to say whether fuzzy matching or Named Entity Resolution would be easier, so it's probably best to give them both a shot.

Sorry for not including resources explicitly for C# - that said, the techniques here are usually more important than the implementations.