forked from kentonl/e2e-coref
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minimize.py
executable file
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minimize.py
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#!/usr/bin/env python
import re
import os
import sys
import json
import tempfile
import subprocess
import collections
import util
import conll
class DocumentState(object):
def __init__(self):
self.doc_key = None
self.text = []
self.text_speakers = []
self.speakers = []
self.sentences = []
self.clusters = collections.defaultdict(list)
self.stacks = collections.defaultdict(list)
def assert_empty(self):
assert self.doc_key is None
assert len(self.text) == 0
assert len(self.text_speakers) == 0
assert len(self.sentences) == 0
assert len(self.speakers) == 0
assert len(self.clusters) == 0
assert len(self.stacks) == 0
def assert_finalizable(self):
assert self.doc_key is not None
assert len(self.text) == 0
assert len(self.text_speakers) == 0
assert len(self.sentences) > 0
assert len(self.speakers) > 0
assert all(len(s) == 0 for s in self.stacks.values())
def finalize(self):
merged_clusters = []
for c1 in self.clusters.values():
existing = None
for m in c1:
for c2 in merged_clusters:
if m in c2:
existing = c2
break
if existing is not None:
break
if existing is not None:
print("Merging clusters (shouldn't happen very often.)")
existing.update(c1)
else:
merged_clusters.append(set(c1))
merged_clusters = [list(c) for c in merged_clusters]
all_mentions = util.flatten(merged_clusters)
assert len(all_mentions) == len(set(all_mentions))
return {
"doc_key": self.doc_key,
"sentences": self.sentences,
"speakers": self.speakers,
"clusters": merged_clusters
}
def normalize_word(word):
if word == "/." or word == "/?":
return word[1:]
else:
return word
def handle_line(line, document_state):
begin_document_match = re.match(conll.BEGIN_DOCUMENT_REGEX, line)
if begin_document_match:
document_state.assert_empty()
document_state.doc_key = conll.get_doc_key(begin_document_match.group(1), begin_document_match.group(2))
return None
elif line.startswith("#end document"):
document_state.assert_finalizable()
return document_state.finalize()
else:
row = line.split()
if len(row) == 0:
document_state.sentences.append(tuple(document_state.text))
del document_state.text[:]
document_state.speakers.append(tuple(document_state.text_speakers))
del document_state.text_speakers[:]
return None
assert len(row) >= 12
word = normalize_word(row[3])
coref = row[-1]
doc_key = conll.get_doc_key(row[0], row[1])
speaker = row[9]
word_index = len(document_state.text) + sum(len(s) for s in document_state.sentences)
document_state.text.append(word)
document_state.text_speakers.append(speaker)
if coref == "-":
return None
for segment in coref.split("|"):
if segment[0] == "(":
if segment[-1] == ")":
cluster_id = int(segment[1:-1])
document_state.clusters[cluster_id].append((word_index, word_index))
else:
cluster_id = int(segment[1:])
document_state.stacks[cluster_id].append(word_index)
else:
cluster_id = int(segment[:-1])
start = document_state.stacks[cluster_id].pop()
document_state.clusters[cluster_id].append((start, word_index))
return None
def minimize_partition(name, language, extension):
input_path = "{}.{}.{}".format(name, language, extension)
output_path = "{}.{}.jsonlines".format(name, language)
count = 0
print("Minimizing {}".format(input_path))
with open(input_path, "r") as input_file:
with open(output_path, "w") as output_file:
document_state = DocumentState()
for line in input_file.readlines():
document = handle_line(line, document_state)
if document is not None:
output_file.write(json.dumps(document))
output_file.write("\n")
count += 1
document_state = DocumentState()
print("Wrote {} documents to {}".format(count, output_path))
def minimize_language(language):
minimize_partition("dev", language, "v4_auto_conll")
minimize_partition("train", language, "v4_auto_conll")
minimize_partition("test", language, "v4_gold_conll")
if __name__ == "__main__":
minimize_language("english")