nltk.ne_chunk returns a nested nltk.tree.Tree object so you would have to traverse the Tree object to get to the NEs.

Take a look at Named Entity Recognition with Regular Expression: NLTK

>>> from nltk import ne_chunk, pos_tag, word_tokenize
>>> from nltk.tree import Tree
>>> 
>>> def get_continuous_chunks(text):
...     chunked = ne_chunk(pos_tag(word_tokenize(text)))
...     continuous_chunk = []
...     current_chunk = []
...     for i in chunked:
...             if type(i) == Tree:
...                     current_chunk.append(" ".join([token for token, pos in i.leaves()]))
...             if current_chunk:
...                     named_entity = " ".join(current_chunk)
...                     if named_entity not in continuous_chunk:
...                             continuous_chunk.append(named_entity)
...                             current_chunk = []
...             else:
...                     continue
...     return continuous_chunk
... 
>>> my_sent = "WASHINGTON -- In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."
>>> get_continuous_chunks(my_sent)
['WASHINGTON', 'New York', 'Loretta E. Lynch', 'Brooklyn']


>>> my_sent = "How's the weather in New York and Brooklyn"
>>> get_continuous_chunks(my_sent)
['New York', 'Brooklyn']

Are you committed to using NLTK/Python? I ran into the same problems as you, and had much better results using Stanford's named-entity recognizer: http://nlp.stanford.edu/software/CRF-NER.shtml. The process for training the classifier using your own data is very well-documented in the FAQ.

If you really need to use NLTK, I'd hit up the mailing list for some advice from other users: http://groups.google.com/group/nltk-users.

Hope this helps!

The default NE chunker in nltk is a maximum entropy chunker trained on the ACE corpus (http://catalog.ldc.upenn.edu/LDC2005T09). It has not been trained to recognise dates and times, so you need to train your own classifier if you want to do that.

Have a look at http://mattshomepage.com/articles/2016/May/23/nltk_nec/, the whole process is explained very well.

Also, there is a module called timex in nltk_contrib which might help you with your needs. https://github.com/nltk/nltk_contrib/blob/master/nltk_contrib/timex.py