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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) |
This code giving me syntax error
What is the expected output. I have just started learning nlp. I executed the code and its giving me blank array. I have copied random English text in sample.txt file. Waiting for reply. :)
@mihir19297: have a look at the following link, the code given in this link is a small variation of the code above, but described with an example and expected output:
https://stackoverflow.com/questions/36255291/extract-city-names-from-text-using-python
It gives the wrong output. it return's the Author workshop from txt file instead of Jay Pratap Pandey. How Can i get correct output?
How would you modify the code to exclude Name Entities.
You have to download the package using the following command in terminal:
import nltk
nltk.donwload('batch_ne_chunk')
Hey, can you attach the sample.txt
file you used with this code? I am getting an empty set
Hey, can you attach the
sample.txt
file you used with this code? I am getting an empty set
Sorry. I didn't understand what you asked?
Hi and thanks for the code. I tried the version 'ririw commented on 3 Jul 2015'. I got syntax error on the last line where it was converting to unique names. If I deleted set then it worked and that was actually better for me because my need was to list the names by frequency. I tried it on an Icelandic saga, Laxdæla ant it worked fine. I added a dictionary to achieve unique names and a line to sort them by value. Here is the adapted code:
import nltk
with open('laxd.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.ne_chunk_sents(tagged_sentences, binary=True)
def extract_entity_names(t):
entity_names = []
if hasattr(t, 'label') and t.label:
if t.label() == '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 = []
names = {}
for tree in chunked_sentences:
# Print results per sentence
# print extract_entity_names(tree)
entity_names.extend(extract_entity_names(tree))
for w in entity_names:
names[w] = names.get(w, 0) +1
Print all entity names
print(sorted(names.items(), key=lambda x:x[1], reverse=True))
use
import nltk nltk.download()
within python or be specific to specify a NLTK library like,nltk.download('stopwords')
I didn't work for me for some reason, when I tried installing the whole nltk package.