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ontonotes.py
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ontonotes.py
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import re
import os
import sys
import codecs
from collections import defaultdict
import numpy as np
sys.path.append("/home/danniel/Desktop/CONLL2012-intern")
from load_conll import load_data
from pstree import PSTree
from rnn import Node
dataset = "ontonotes"
character_file = os.path.join(dataset, "character.txt")
word_file = os.path.join(dataset, "word.txt")
pos_file = os.path.join(dataset, "pos.txt")
ne_file = os.path.join(dataset, "ne.txt")
pretrained_word_file = os.path.join(dataset, "word.npy")
pretrained_embedding_file = os.path.join(dataset, "embedding.npy")
data_path_prefix = "/home/danniel/Desktop/CONLL2012-intern/conll-2012/v4/data"
test_auto_data_path_prefix = "/home/danniel/Downloads/wu_conll_test/v9/data"
data_path_suffix = "data/english/annotations"
glove_file = "/home/danniel/Downloads/glove.840B.300d.txt"
senna_path = "/home/danniel/Downloads/senna/hash"
dbpedia_path = "/home/danniel/Desktop/dbpedia_lexicon"
lexicon_meta_list = [
{"ne": "PERSON", "encoding": "iso8859-15", "clean": os.path.join(dataset, "senna_PER.txt"), "raw": os.path.join(senna_path, "ner.per.lst")},
{"ne": "ORG", "encoding": "iso8859-15", "clean": os.path.join(dataset, "senna_ORG.txt"), "raw": os.path.join(senna_path, "ner.org.lst")},
{"ne": "LOC", "encoding": "iso8859-15", "clean": os.path.join(dataset, "senna_LOC.txt"), "raw": os.path.join(senna_path, "ner.loc.lst")}
#{"ne": "WORK_OF_ART", "encoding": "iso8859-15", "clean": os.path.join(dataset, "senna_WOR.txt"), "raw": os.path.join(senna_path, "ner.misc.lst")},
#{"ne": "PERSON", "encoding": "utf8", "clean": os.path.join(dataset, "dbpedia_PER.txt"), "raw": os.path.join(dbpedia_path, "dbpedia_person.txt")},
#{"ne": "ORG", "encoding": "utf8", "clean": os.path.join(dataset, "dbpedia_ORG.txt"), "raw": os.path.join(dbpedia_path, "dbpedia_organisation.txt")},
#{"ne": "LOC", "encoding": "utf8", "clean": os.path.join(dataset, "dbpedia_LOC.txt"), "raw": os.path.join(dbpedia_path, "dbpedia_place.txt")}
#{"ne": "WORK_OF_ART", "encoding": "utf8", "clean": os.path.join(dataset, "dbpedia_WOR.txt"), "raw": os.path.join(dbpedia_path, "dbpedia_work.txt")}
]
def log(msg):
sys.stdout.write(msg)
sys.stdout.flush()
return
def read_list_file(file_path, encoding="utf8"):
log("Read %s..." % file_path)
with codecs.open(file_path, "r", encoding=encoding) as f:
line_list = f.read().splitlines()
line_to_index = {line: index for index, line in enumerate(line_list)}
log(" %d lines\n" % len(line_to_index))
return line_list, line_to_index
def extract_vocabulary_and_alphabet():
log("extract_vocabulary_and_alphabet()...")
character_set = set()
word_set = set()
for split in ["train", "development", "test"]:
full_path = os.path.join(data_path_prefix, split, data_path_suffix)
config = {"file_suffix": "gold_conll", "dir_prefix": full_path}
raw_data = load_data(config)
for document in raw_data:
for part in raw_data[document]:
for index, sentence in enumerate(raw_data[document][part]["text"]):
for word in sentence:
for character in word:
character_set.add(character)
word_set.add(word)
with codecs.open(word_file, "w", encoding="utf8") as f:
for word in sorted(word_set):
f.write(word + '\n')
with codecs.open(character_file, "w", encoding="utf8") as f:
for character in sorted(character_set):
f.write(character + '\n')
log(" done\n")
return
def extract_glove_embeddings():
log("extract_glove_embeddings()...")
_, word_to_index = read_list_file(word_file)
word_list = []
embedding_list = []
with open(glove_file, "r") as f:
for line in f:
line = line.strip().split()
word = line[0]
if word not in word_to_index: continue
embedding = np.array([float(i) for i in line[1:]])
word_list.append(word)
embedding_list.append(embedding)
np.save(pretrained_word_file, word_list)
np.save(pretrained_embedding_file, embedding_list)
log(" %d pre-trained words\n" % len(word_list))
return
def traverse_tree(tree, ner_raw_data, head_raw_data, text_raw_data, lexicon_list, span_set):
pos = tree.label
span = tree.span
head = tree.head if hasattr(tree, "head") else head_raw_data[(span, pos)][1]
ne = ner_raw_data[span] if span in ner_raw_data else "NONE"
constituent = " ".join(text_raw_data[span[0]:span[1]]).lower()
span_set.add(span)
for index, lexicon in enumerate(lexicon_list):
if constituent in lexicon:
lexicon[constituent][0] += 1
if ne == lexicon_meta_list[index]["ne"]:
lexicon[constituent][1] += 1
# Binarize children
if len(tree.subtrees) > 2:
side_child_pos = tree.subtrees[-1].label
side_child_span = tree.subtrees[-1].span
side_child_head = head_raw_data[(side_child_span, side_child_pos)][1]
if side_child_head != head:
sub_subtrees = tree.subtrees[:-1]
else:
sub_subtrees = tree.subtrees[1:]
new_span = (sub_subtrees[0].span[0], sub_subtrees[-1].span[1])
new_tree = PSTree(label=pos, span=new_span, subtrees=sub_subtrees)
new_tree.head = head
if side_child_head != head:
tree.subtrees = [new_tree, tree.subtrees[-1]]
else:
tree.subtrees = [tree.subtrees[0], new_tree]
# Process children
for subtree in tree.subtrees:
traverse_tree(subtree, ner_raw_data, head_raw_data, text_raw_data, lexicon_list, span_set)
return
def traverse_pyramid(ner_raw_data, text_raw_data, lexicon_list, span_set):
max_dense_span = 3
# Start from bigram, since all unigrams are already covered by parses
for span_length in range(2, 1+max_dense_span):
for span_start in range(0, 1+len(text_raw_data)-span_length):
span = (span_start, span_start+span_length)
if span in span_set: continue
ne = ner_raw_data[span] if span in ner_raw_data else "NONE"
constituent = " ".join(text_raw_data[span[0]:span[1]]).lower()
for index, lexicon in enumerate(lexicon_list):
if constituent in lexicon:
lexicon[constituent][0] += 1
if ne == lexicon_meta_list[index]["ne"]:
lexicon[constituent][1] += 1
return
def extract_clean_lexicon():
lexicon_list = []
print "\nReading raw lexicons..."
for meta in lexicon_meta_list:
lexicon_list.append(read_list_file(meta["raw"], encoding=meta["encoding"])[1])
print "-"*50 + "\n ne phrases shortest\n" + "-"*50
for index, lexicon in enumerate(lexicon_list):
for phrase in lexicon:
lexicon[phrase] = [0.,0.]
shortest_phrase = min(lexicon.iterkeys(), key=lambda phrase: len(phrase))
print "%12s %8d %s" % (lexicon_meta_list[index]["ne"], len(lexicon), shortest_phrase)
print "Reading training data..."
data_split_list = ["train", "validate"]
annotation_method_list = ["gold", "auto"]
raw_data = {}
for split in data_split_list:
raw_data[split] = {}
for method in annotation_method_list:
if split == "validate":
data_path_root = "development"
else:
data_path_root = split
full_path = os.path.join(data_path_prefix, data_path_root, data_path_suffix)
config = {"file_suffix": "%s_conll" % method, "dir_prefix": full_path}
raw_data[split][method] = load_data(config)
log("\nCleaning lexicon by training data...")
for split in data_split_list:
for document in raw_data[split]["auto"]:
for part in raw_data[split]["auto"][document]:
ner_raw_data = defaultdict(lambda: {})
for k, v in raw_data[split]["gold"][document][part]["ner"].iteritems():
ner_raw_data[k[0]][(k[1], k[2])] = v
for index, parse in enumerate(raw_data[split]["auto"][document][part]["parses"]):
text_raw_data = raw_data[split]["auto"][document][part]["text"][index]
if parse.subtrees[0].label == "NOPARSE": continue
head_raw_data = raw_data[split]["auto"][document][part]["heads"][index]
span_set = set()
traverse_tree(parse, ner_raw_data[index], head_raw_data, text_raw_data,
lexicon_list, span_set)
traverse_pyramid(ner_raw_data[index], text_raw_data, lexicon_list, span_set)
log(" done\n")
print "-"*50 + "\n ne phrases shortest\n" + "-"*50
for index, lexicon in enumerate(lexicon_list):
for phrase, count in lexicon.items():
if count[0]>0 and count[1]/count[0]<0.1:
del lexicon[phrase]
shortest_phrase = min(lexicon.iterkeys(), key=lambda phrase: len(phrase))
print "%12s %8d %s" % (lexicon_meta_list[index]["ne"], len(lexicon), shortest_phrase)
for index, lexicon in enumerate(lexicon_list):
meta = lexicon_meta_list[index]
with codecs.open(meta["clean"], "w", encoding=meta["encoding"]) as f:
for phrase in sorted(lexicon.iterkeys()):
f.write("%s\n" % phrase)
return
def construct_node(node, tree, ner_raw_data, head_raw_data, text_raw_data,
character_to_index, word_to_index, pos_to_index, lexicon_list,
pos_count, ne_count, pos_ne_count, lexicon_hits, span_to_node, under_ne):
pos = tree.label
word = tree.word
span = tree.span
head = tree.head if hasattr(tree, "head") else head_raw_data[(span, pos)][1]
ne = ner_raw_data[span] if span in ner_raw_data else "NONE"
constituent = " ".join(text_raw_data[span[0]:span[1]]).lower()
# Process pos info
node.pos_index = pos_to_index[pos]
pos_count[pos] += 1
node.pos = pos #YOLO
# Process word info
node.word_split = [character_to_index[character] for character in word] if word else []
node.word_index = word_to_index[word] if word else -1
node.word = word if word else "" # YOLO
# Process head info
node.head_split = [character_to_index[character] for character in head]
node.head_index = word_to_index[head]
node.head = head # YOLO
# Process ne info
node.under_ne = under_ne
node.ne = ne
if ne != "NONE":
under_ne = True
if not node.parent or node.parent.span!=span:
ne_count[ne] += 1
pos_ne_count[pos] += 1
"""
if hasattr(tree, "head"):
print " ".join(text_raw_data)
print " ".join(text_raw_data[span[0]:span[1]])
print ne
print node.parent.head
raw_input()
"""
# Process span info
node.span = span
node.span_length = span[1] - span[0]
span_to_node[span] = node
# Process lexicon info
node.lexicon_hit = [0] * len(lexicon_list)
hits = 0
for index, lexicon in enumerate(lexicon_list):
if constituent in lexicon:
lexicon[constituent] += 1
node.lexicon_hit[index] = 1
hits = 1
lexicon_hits[0] += hits
# Binarize children
if len(tree.subtrees) > 2:
side_child_pos = tree.subtrees[-1].label
side_child_span = tree.subtrees[-1].span
side_child_head = head_raw_data[(side_child_span, side_child_pos)][1]
if side_child_head != head:
sub_subtrees = tree.subtrees[:-1]
else:
sub_subtrees = tree.subtrees[1:]
new_span = (sub_subtrees[0].span[0], sub_subtrees[-1].span[1])
new_tree = PSTree(label=pos, span=new_span, subtrees=sub_subtrees)
new_tree.head = head
if side_child_head != head:
tree.subtrees = [new_tree, tree.subtrees[-1]]
else:
tree.subtrees = [tree.subtrees[0], new_tree]
# Process children
nodes = 1
for subtree in tree.subtrees:
child = Node()
node.add_child(child)
child_nodes = construct_node(child, subtree, ner_raw_data, head_raw_data, text_raw_data,
character_to_index, word_to_index, pos_to_index, lexicon_list,
pos_count, ne_count, pos_ne_count, lexicon_hits, span_to_node, under_ne)
nodes += child_nodes
return nodes
def create_dense_nodes(ner_raw_data, text_raw_data, pos_to_index, lexicon_list,
pos_count, ne_count, pos_ne_count, lexicon_hits, span_to_node):
node_list = []
max_dense_span = 3
# Start from bigram, since all unigrams are already covered by parses
for span_length in range(2, 1+max_dense_span):
for span_start in range(0, 1+len(text_raw_data)-span_length):
span = (span_start, span_start+span_length)
if span in span_to_node: continue
pos = "NONE"
ne = ner_raw_data[span] if span in ner_raw_data else "NONE"
constituent = " ".join(text_raw_data[span[0]:span[1]]).lower()
# span, child
# TODO: sibling
node = Node(family=1)
node_list.append(node)
node.span = span
node.span_length = span_length
span_to_node[span] = node
node.child_list = [span_to_node[(span[0],span[1]-1)], span_to_node[(span[0]+1,span[1])]]
# word, head, pos
node.pos_index = pos_to_index[pos]
pos_count[pos] += 1
node.word_split = []
node.word_index = -1
node.head_split = []
node.head_index = -1
# ne
node.ne = ne
if ne != "NONE":
ne_count[ne] += 1
pos_ne_count[pos] += 1
# lexicon
node.lexicon_hit = [0] * len(lexicon_list)
hits = 0
for index, lexicon in enumerate(lexicon_list):
if constituent in lexicon:
lexicon[constituent] += 1
node.lexicon_hit[index] = 1
hits = 1
lexicon_hits[0] += hits
return node_list
def get_tree_data(raw_data, character_to_index, word_to_index, pos_to_index, lexicon_list):
log("get_tree_data()...")
""" Get tree structured data from CoNLL 2012
Stores into Node data structure
"""
tree_pyramid_list = []
ner_list = []
word_count = 0
pos_count = defaultdict(lambda: 0)
ne_count = defaultdict(lambda: 0)
pos_ne_count = defaultdict(lambda: 0)
lexicon_hits = [0]
for document in raw_data["auto"]:
for part in raw_data["auto"][document]:
ner_raw_data = defaultdict(lambda: {})
for k, v in raw_data["gold"][document][part]["ner"].iteritems():
ner_raw_data[k[0]][(k[1], k[2])] = v
for index, parse in enumerate(raw_data["auto"][document][part]["parses"]):
text_raw_data = raw_data["auto"][document][part]["text"][index]
word_count += len(text_raw_data)
if parse.subtrees[0].label == "NOPARSE": continue
head_raw_data = raw_data["auto"][document][part]["heads"][index]
root_node = Node()
span_to_node = {}
nodes = construct_node(
root_node, parse, ner_raw_data[index], head_raw_data, text_raw_data,
character_to_index, word_to_index, pos_to_index, lexicon_list,
pos_count, ne_count, pos_ne_count, lexicon_hits, span_to_node, False)
root_node.nodes = nodes
root_node.text_raw_data = text_raw_data #YOLO
additional_node_list = []
"""
additional_node_list = create_dense_nodes(
ner_raw_data[index], text_raw_data,
pos_to_index, lexicon_list,
pos_count, ne_count, pos_ne_count, lexicon_hits, span_to_node)
"""
tree_pyramid_list.append((root_node, additional_node_list))
ner_list.append(ner_raw_data[index])
log(" %d sentences\n" % len(tree_pyramid_list))
return (tree_pyramid_list, ner_list, word_count, pos_count, ne_count, pos_ne_count,
lexicon_hits[0])
def label_tree_data(node, pos_to_index, ne_to_index):
node.y = ne_to_index[node.ne]
# node.y = ne_to_index[":".join(node.ner)]
for child in node.child_list:
label_tree_data(child, pos_to_index, ne_to_index)
return
def read_dataset(data_split_list = ["train", "validate", "test"]):
# Read all raw data
annotation_method_list = ["gold", "auto"]
raw_data = {}
for split in data_split_list:
raw_data[split] = {}
for method in annotation_method_list:
if split == "test" and method == "auto":
full_path = os.path.join(test_auto_data_path_prefix, "test", data_path_suffix)
else:
if split == "validate":
data_path_root = "development"
else:
data_path_root = split
full_path = os.path.join(data_path_prefix, data_path_root, data_path_suffix)
config = {"file_suffix": "%s_conll" % method, "dir_prefix": full_path}
raw_data[split][method] = load_data(config)
# Read lists of annotations
character_list, character_to_index = read_list_file(character_file)
word_list, word_to_index = read_list_file(word_file)
pos_list, pos_to_index = read_list_file(pos_file)
ne_list, ne_to_index = read_list_file(ne_file)
pos_to_index["NONE"] = len(pos_to_index)
# Read lexicon
lexicon_list = []
for meta in lexicon_meta_list:
lexicon_list.append(read_list_file(meta["raw"], encoding=meta["encoding"])[1])
#lexicon_list.append(read_list_file(meta["clean"], encoding=meta["encoding"])[1])
for lexicon in lexicon_list:
for phrase in lexicon:
lexicon[phrase] = 0
# Build a tree structure for each sentence
data = {}
word_count = {}
pos_count = {}
ne_count = {}
pos_ne_count = {}
lexicon_hits = {}
for split in data_split_list:
(tree_pyramid_list, ner_list,
word_count[split], pos_count[split], ne_count[split], pos_ne_count[split],
lexicon_hits[split]) = get_tree_data(raw_data[split],
character_to_index, word_to_index, pos_to_index, lexicon_list)
#data[split] = [tree_list, ner_list]
data[split] = {"tree_pyramid_list": tree_pyramid_list, "ner_list": ner_list}
for index, lexicon in enumerate(lexicon_list):
with codecs.open("tmp_%d.txt" % index, "w", encoding="utf8") as f:
for phrase, count in sorted(lexicon.iteritems(), key=lambda x: (-x[1], x[0])):
if count == 0: break
f.write("%9d %s\n" % (count, phrase))
# Show statistics of each data split
print "-" * 80
print "%10s%10s%9s%9s%7s%12s%13s" % ("split", "sentence", "token", "node", "NE", "spanned_NE",
"lexicon_hit")
print "-" * 80
for split in data_split_list:
print "%10s%10d%9d%9d%7d%12d%13d" % (split,
len(data[split]["tree_pyramid_list"]),
word_count[split],
sum(pos_count[split].itervalues()),
sum(len(ner) for ner in data[split]["ner_list"]),
sum(ne_count[split].itervalues()),
lexicon_hits[split])
# Show POS distribution
total_pos_count = defaultdict(lambda: 0)
for split in data_split_list:
for pos in pos_count[split]:
total_pos_count[pos] += pos_count[split][pos]
nodes = sum(total_pos_count.itervalues())
print "\nTotal %d nodes" % nodes
print "-"*80 + "\n POS count ratio\n" + "-"*80
for pos, count in sorted(total_pos_count.iteritems(), key=lambda x: x[1], reverse=True)[:10]:
print "%6s %7d %5.1f%%" % (pos, count, count*100./nodes)
# Show NE distribution in [train, validate]
total_ne_count = defaultdict(lambda: 0)
for split in data_split_list:
if split == "test": continue
for ne in ne_count[split]:
total_ne_count[ne] += ne_count[split][ne]
nes = sum(total_ne_count.itervalues())
print "\nTotal %d spanned named entities in [train, validate]" % nes
print "-"*80 + "\n NE count ratio\n" + "-"*80
for ne, count in sorted(total_ne_count.iteritems(), key=lambda x: x[1], reverse=True):
print "%12s %6d %5.1f%%" % (ne, count, count*100./nes)
# Show POS-NE distribution in [train, validate]
total_pos_ne_count = defaultdict(lambda: 0)
for split in data_split_list:
if split == "test": continue
for pos in pos_ne_count[split]:
total_pos_ne_count[pos] += pos_ne_count[split][pos]
print "-"*80 + "\n POS NE total ratio\n" + "-"*80
for pos, count in sorted(total_pos_ne_count.iteritems(), key=lambda x: x[1], reverse=True)[:10]:
total = total_pos_count[pos]
print "%6s %6d %7d %5.1f%%" % (pos, count, total, count*100./total)
# Compute the mapping to labels
ne_to_index["NONE"] = len(ne_to_index)
# Add label to nodes
for split in data_split_list:
for tree, pyramid in data[split]["tree_pyramid_list"]:
label_tree_data(tree, pos_to_index, ne_to_index)
for node in pyramid:
node.y = ne_to_index[node.ne]
return (data, word_list, ne_list,
len(character_to_index), len(pos_to_index), len(ne_to_index), len(lexicon_list))
if __name__ == "__main__":
#extract_vocabulary_and_alphabet()
#extract_glove_embeddings()
#extract_clean_lexicon()
read_dataset()
exit()