KNIME comes with several native nodes for performing different tagging tasks, like POS tagging or named entity recognition. In order to use the identified tags or terms, you can use the Bag of Words node, which produces terms (not words) and associated tags. However, this approach does not detail which tag is associated to each word, and neither the order of the tags (or words).
Therefore, if you want to extract features like 'POS tags +/- N words with respect to the actual word' (eg. a words window), how can you?
For example, for 'That city was New York', I would like KNIME to produce an ordered list like: (where the last NN would be a named entity).
Yes that is a problem. I faced the same situation last week.
If you want get just the POS by word you can link the Tags to String node to the bag of words node's output, but I think this approach fails for large documents because would be possible get different POS for the same word (POS value for a certain word is decided using word's context) since bag of words node shows unique terms by document.
Now, in order to get this windows ('POS tags +/- N words with respect to the actual word') a solution for me was connect Freeling through command line (external toold node or by python/java code) and get the corresponding tag per word just looking in the returned vector.