I would be curious to see examples of NLP techniques (in particular, Named entity recognition, Relationship extraction and Topic modeling) applied to generate descriptive metadata and any thoughts on what techniques and tools are producing useful results. I'm up for hearing about experimental work, but I would be particularly interested to hear about any projects making good use of NLR to generate descriptive metadata from full text content at scale.
I am really just starting to get familiar with this field, but could this paper be of use?
LIGHTWEIGHT PARSING OF NATURAL LANGUAGE METADATA Aliaksandr Autayeu, Fausto Giunchiglia, Pierre Andrews, Ju Qi
Also: in Natural Language Processing for Digital Libraries (NLP4DL) Workshop, Viareggio, Italy, June 15th 2009.
From the Conclusions:
They used OpenNLP, of which I suppose you are aware
http://en.wikipedia.org/wiki/OpenNLP (The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution).
Hope this helps - I used to work there so probably I am biased, but it seemed interesting.