You can train your own named entity chunker using the
ieer corpus, which stands for Information Extraction: Entity Recognition. It takes a bit of extra work, though, because the ieer corpus has chunk trees but no part-of-speech tags for words.
Using the ieertree2conlltags() and ieer_chunked_sents() functions in chunkers.py, we can create named entity chunk trees from the ieer corpus to train the ClassifierChunker class created in the Classification-based chunking recipe:
import nltk.tag
from nltk.chunk.util import conlltags2tree
from nltk.corpus import ieer
def ieertree2conlltags(tree, tag=nltk.tag.pos_tag):
words, ents = zip(*tree.pos())
iobs = []
prev = None
for ent in ents:
if ent == tree.label():
iobs.append('O')
prev = None
elif prev == ent:
iobs.append('I-%s' % ent)
else:
iobs.append('B-%s' % ent)
prev = ent
words, tags = zip(*tag(words))
return zip(words, tags, iobs)
def ieer_chunked_sents...