I'm currently searching for labeled datasets to use to train a model to extract named entities from informal text (think something similar to tweets). Because capitalization and grammar are often lacking in the documents in my dataset, I'm looking for out of domain data that's a bit more "informal" than the news articles and journal entries that many of today's state of the art named entity recognition systems are trained on. Any recommendations? So far I've only been able to locate 50k tokens from twitter published here: https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt
Although the entity set is more restricted than you are looking for, the following might be useful:
The data is referenced in the following paper by Ashwini and Choi, which discusses and evaluates the general approach: http://arxiv.org/abs/1408.0782
The Social Security administration provides datasets on the top 1000 popular (baby) names per year. This would be a good starting resource for recognizing first names in feeds.
I would also consider adding in recognizing city names. There are a lot of free datasets. Below is a link to one of my own compilations built from the USGS and US NGA/GNS datasets.