Improving parsing of unstructured text

I am parsing contract announcements into columns to capture the company, the amount awarded, the description of the project awarded, etc. A raw example can be found here.

I wrote a script using regular expressions to do this but over time contingencies arise that I have to account for which bar the regexp method from being a long term solution. I have been reading up on NLTK and it seems there are two ways to go about using NLTK to solve my problem:

1. chunk the announcements using RegexpParser expressions - this might be a weak solution if two different fields I want to capture have the same sentence structure.
2. take n announcements, tokenize and run the n announcements through the pos tagger, manually tag the parts of the announcements I want to capture using the IOB format and then use those tagged announcements to train an NER model. A method discussed here

Before I go about manually tagging announcements I want to gauge

1. that 2 is a reasonable solution
2. if there are existing tagged corpus that might be useful to train my model
3. knowing that accuracy improves with training data size, how many manually tagged announcements I should start with.

Here's an example of how I am building the training set. If there are any apparent flaws please let me know.