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Converting annotations to brat format
#!/usr/bin/env python
Convert a document in the corpus to the brat ANN format.
Call like ' <path>/<document id>'. Outputs two files in the current
directory <file>.txt and <file>.ann, where <file> is names from the command-line
import argparse
import json
import operator
import sys
from itertools import count
def accumulate(iterable, func=operator.add):
'Return running totals'
# accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
total = next(it)
yield total
for element in it:
total = func(total, element)
yield total
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--verbose', dest='verbose', action='store_true')
args = parser.parse_args()
#Input file paths
base_file_path = args.base_file_path
sentence_file_path = '.'.join((base_file_path, 'sentences'))
pos_file_path = '.'.join((base_file_path, 'pos'))
dep_file_path = '.'.join((base_file_path, 'dep'))
#Read in sentences, POS, and dependencies
sentences = [[w for w in l.split() if w.strip()]
for l in open(sentence_file_path) if l.strip()]
pos_strings = [l for l in open(pos_file_path)]
dep_strings = [l for l in open(dep_file_path)]
#Calculate output file names
base_out_name = '_'.join( base_file_path.split('/')[-4:] )
txt_out_name = '.'.join((base_out_name, 'txt'))
ann_out_name = '.'.join((base_out_name, 'ann'))
# Add a dummy root token to each sentence
sentences = [ ['ROOT'] + s for s in sentences ]
# Calculate offsets (with extra 1 for newline char)
sentence_lengths = [ len(' '.join(s)) + 1 for s in sentences ]
sentence_offsets = [0] + list(accumulate(sentence_lengths[:-1]))
# within-sentence offsets (with extra 1 for space)
token_lengths = [ [ len(w) + 1 for w in s ] for s in sentences ]
token_offsets = [ [0] + list(accumulate(tls[:-1])) for tls in token_lengths ]
# Dump the sentences to *.txt
with open(txt_out_name, 'w') as txt_out:
for s in sentences:
print >>txt_out, ' '.join(s)
# Output annotations to *.ann
# POS replacement dict
pos_replacements = {
"''": "QUOTE_CLOSE",
"``": "QUOTE_OPEN",
'PRP': 'PRON',
',': 'COMMA',
':': 'PUNCT',
'.': 'PERIOD',
'$': 'MONEY',
'WP$': 'WP_POS',
# ID generators
def token_id_gen():
for n in count(1):
yield "T{}".format(n)
token_ids = token_id_gen()
def relation_id_gen():
for n in count(1):
yield "R{}".format(n)
relation_ids = relation_id_gen()
ann_out = open(ann_out_name, 'w')
for i, ws, pstr, dstr in zip(count(0), sentences, pos_strings, dep_strings):
#get offsets
soff = sentence_offsets[i]
woffs = token_offsets[i]
#parse POS and dependency annotations
ps = json.loads(pstr)
ds = json.loads(dstr)
#correct POS for dummy token
ps = ['ROOT'] + ps
#correct dependency token indices for dummy token
ds = [ (r, h+1, d+1) for r, h, d in ds ]
#create ANN tokens
ann_tokens = [
pos=pos_replacements.get(p, p),
start=soff + b,
end=soff + b + len(w) #ANN offsets are one-past-end
for t, p, b, w in zip(token_ids, ps, woffs, ws)
ann_relations = [
for k, r, h, d in [(k,) + tup for k,tup in zip(relation_ids, ds)]
#output annotations
for tok in ann_tokens:
print >>ann_out, "{tid}\t{pos} {start} {end}\t{word}".format(**tok)
for rel in ann_relations:
print >>ann_out, "{rid}\t{rel} Arg1:{hid} Arg2:{did}".format(**rel)
except Exception as e:
# print >>sys.stderr, '{file}:{sent}:Exception: {ex}'.format(file=base_file_path,
# sent=i,
# ex=e)
# sys.exit(1)

Thanks for putting this up. We are trying to take the XML output from CoreNLP and feed it into brat (locally on our system - we can't do it online). It looks like your script expects 4 input files and I can't figure out what the .pos .sentences and .dep files are or what generates them. When we feed text to CoreNLP all we get back is the .xml file.

Any help greatly appreciated.
-JK Scheinberg

cordarei commented Jan 5, 2014


Yeah, I know the script isn't exactly useful as is -- it's meant to show an example of outputting the format brat expects. The most interesting parts are probably calculating the character offsets for the part-of-speech annotations and creating a unique ID for each annotation.

There are three input files: a file with tokenized text, one sentence per line; a file with POS tags (each line is a list of POS tags corresponding to a sentence in the text file; and a file with dependency relations (each line is a list of 3-element [label, head_index, daughter_index] lists, one for each sentence). If the CoreNLP output has character offsets you really don't need anything here, you can just read the XML and output the annotations (see lines 141-145 for the format). I ended up with this format because JSON is a little bit easier to deal with than XML.

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