Named Entity Recognition with Regular Expression: NLTK - regex

Question

I have been playing with NLTK toolkit. I come across this problem a lot and searched for solution online but nowhere I got a satisfying answer. So I am putting my query here.
Many times NER doesn't tag consecutive NNPs as one NE. I think editing the NER to use RegexpTagger also can improve the NER.
Example:
Input:
Barack Obama is a great person.
Output:
Tree('S', [Tree('PERSON', [('Barack', 'NNP')]), Tree('ORGANIZATION', [('Obama', 'NNP')]), ('is', 'VBZ'), ('a', 'DT'), ('great', 'JJ'), ('person', 'NN'), ('.', '.')])
where as
input:
Former Vice President Dick Cheney told conservative radio host Laura Ingraham that he "was honored" to be compared to Darth Vader while in office.
Output:
Tree('S', [('Former', 'JJ'), ('Vice', 'NNP'), ('President', 'NNP'), Tree('NE', [('Dick', 'NNP'), ('Cheney', 'NNP')]), ('told', 'VBD'), ('conservative', 'JJ'), ('radio', 'NN'), ('host', 'NN'), Tree('NE', [('Laura', 'NNP'), ('Ingraham', 'NNP')]), ('that', 'IN'), ('he', 'PRP'), ('', ''), ('was', 'VBD'), ('honored', 'VBN'), ("''", "''"), ('to', 'TO'), ('be', 'VB'), ('compared', 'VBN'), ('to', 'TO'), Tree('NE', [('Darth', 'NNP'), ('Vader', 'NNP')]), ('while', 'IN'), ('in', 'IN'), ('office', 'NN'), ('.', '.')])
Here Vice/NNP, President/NNP, (Dick/NNP, Cheney/NNP) , is correctly extracted.
So I think if nltk.ne_chunk is used first and then if two consecutive trees are NNP there are high chances that both refers to one entity.
Any suggestion will be really appreciated. I am looking for flaws in my approach.
Thanks.

Answer 1

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)))
prev = None
continuous_chunk = []
current_chunk = []
for i in chunked:
if type(i) == Tree:
current_chunk.append(" ".join([token for token, pos in i.leaves()]))
elif current_chunk:
named_entity = " ".join(current_chunk)
if named_entity not in continuous_chunk:
continuous_chunk.append(named_entity)
current_chunk = []
else:
continue
if continuous_chunk:
named_entity = " ".join(current_chunk)
if named_entity not in continuous_chunk:
continuous_chunk.append(named_entity)
return continuous_chunk
txt = "Barack Obama is a great person."
print get_continuous_chunks(txt)
[out]:
['Barack Obama']
But do note that if the continuous chunk are not supposed to be a single NE, then you would be combining multiple NEs into one. I can't think of such an example off my head but i'm sure it would happen. But if they not continuous, the script above works fine:
>>> txt = "Barack Obama is the husband of Michelle Obama."
>>> get_continuous_chunks(txt)
['Barack Obama', 'Michelle Obama']

Answer 2

There is a bug in #alvas's answer. Fencepost error. Make sure to run that elif check outside of the loop as well so that you don't leave off a NE that occurs at the end of the sentence. So:
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)))
prev = None
continuous_chunk = []
current_chunk = []
for i in chunked:
if type(i) == Tree:
current_chunk.append(" ".join([token for token, pos in i.leaves()]))
elif current_chunk:
named_entity = " ".join(current_chunk)
if named_entity not in continuous_chunk:
continuous_chunk.append(named_entity)
current_chunk = []
else:
continue
if current_chunk:
named_entity = " ".join(current_chunk)
if named_entity not in continuous_chunk:
continuous_chunk.append(named_entity)
current_chunk = []
return continuous_chunk
txt = "Barack Obama is a great person and so is Michelle Obama."
print get_continuous_chunks(txt)

Answer 3

#alvas great answer. It was really helpful. I have tried to capture your solution in a more functional way. Still have to improve on it though.
def conditions(tree_node):
return tree_node.height() == 2
def coninuous_entities(self, input_text, file_handle):
from nltk import ne_chunk, pos_tag, word_tokenize
from nltk.tree import Tree
# Note: Currently, the chunker categorizes only 2 'NNP' together.
docs = input_text.split('\n')
for input_text in docs:
chunked_data = ne_chunk(pos_tag(word_tokenize(input_text)))
child_data = [subtree for subtree in chunked_data.subtrees(filter = self.filter_conditions)]
named_entities = []
for child in child_data:
if type(child) == Tree:
named_entities.append(" ".join([token for token, pos in child.leaves()]))
# Dump all entities to file for now, we will see how to go about that
if file_handle is not None:
file_handle.write('\n'.join(named_entities) + '\n')
return named_entities
Using conditions function one can add many conditions to filter.