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Basic example of using NLTK for name entity extraction.
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import nltk | |
with open('sample.txt', 'r') as f: | |
sample = f.read() | |
sentences = nltk.sent_tokenize(sample) | |
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences] | |
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] | |
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True) | |
def extract_entity_names(t): | |
entity_names = [] | |
if hasattr(t, 'node') and t.node: | |
if t.node == 'NE': | |
entity_names.append(' '.join([child[0] for child in t])) | |
else: | |
for child in t: | |
entity_names.extend(extract_entity_names(child)) | |
return entity_names | |
entity_names = [] | |
for tree in chunked_sentences: | |
# Print results per sentence | |
# print extract_entity_names(tree) | |
entity_names.extend(extract_entity_names(tree)) | |
# Print all entity names | |
#print entity_names | |
# Print unique entity names | |
print set(entity_names) |
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