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3dac39e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | #!/usr/bin/env python3
"""Convert APTNER CoNLL-style BIOES files to our 5-class JSONL format.
Handles noisy tags by extracting the BIOES prefix and base entity type,
then mapping to our 5-class label space.
"""
import json
import re
import sys
from collections import defaultdict
from pathlib import Path
# Label mapping: APTNER type -> our 5-class label (None = DROP)
LABEL_MAP = {
"MAL": "Malware",
"TOOL": "System",
"OS": "System",
"IDTY": "Organization",
"IDTYL": "Organization", # typo variant in data
"APT": "Organization",
"SECTEAM": "Organization",
"VULNAME": "Vulnerability",
"VULID": "Vulnerability",
"FILE": "Indicator",
"URL": "Indicator",
"IP": "Indicator",
"EMAIL": "Indicator",
"SHA2": "Indicator",
"SHA1": "Indicator",
"MD5": "Indicator",
"DOM": "Indicator",
# DROP these
"ACT": None,
"LOC": None,
"TIME": None,
"PROT": None,
"ENCR": None,
}
VALID_BIOES = {"B", "I", "O", "E", "S"}
def parse_tag(raw_tag: str):
"""Parse a potentially noisy tag. Returns (bioes_prefix, entity_type) or ('O', None)."""
raw_tag = raw_tag.strip()
if raw_tag == "O":
return "O", None
# Match standard BIOES-TYPE pattern at start
m = re.match(r'^([BIOES])-([A-Z][A-Z0-9]*)', raw_tag)
if m:
return m.group(1), m.group(2)
# Handle double prefix like E-S-SECTEAM or S-S-SECTEAM
m = re.match(r'^([BIOES])-[BIOES]-([A-Z][A-Z0-9]*)', raw_tag)
if m:
return m.group(1), m.group(2)
return "O", None
def parse_conll_file(path: Path):
"""Parse APTNER CoNLL file into list of (tokens, tags) sentences."""
sentences = []
tokens, tags = [], []
with open(path) as f:
for line in f:
line = line.rstrip("\n")
if not line or line.isspace():
if tokens:
sentences.append((tokens, tags))
tokens, tags = [], []
continue
# Space-separated: token tag (sometimes extra junk after tag)
parts = line.split(" ")
if len(parts) < 2:
# Malformed line - treat as O-tagged token
tokens.append(parts[0])
tags.append("O")
continue
token = parts[0]
# The tag is parts[1], but sometimes there's noise like "E-APT also"
raw_tag = parts[1]
tokens.append(token)
tags.append(raw_tag)
if tokens:
sentences.append((tokens, tags))
return sentences
def tokens_to_text_and_offsets(tokens):
"""Join tokens with spaces and return (text, list_of_char_offsets)."""
offsets = []
pos = 0
for t in tokens:
offsets.append(pos)
pos += len(t) + 1 # +1 for space
text = " ".join(tokens)
return text, offsets
def extract_spans(tokens, tags, offsets):
"""Extract entity spans from BIOES tags, mapped to our label space.
Returns dict like {"Malware: name": [[start, end], ...]}
"""
spans = defaultdict(list)
i = 0
n = len(tokens)
while i < n:
prefix, etype = parse_tag(tags[i])
if prefix == "O" or etype is None:
i += 1
continue
our_label = LABEL_MAP.get(etype)
if our_label is None:
# DROP this entity type
i += 1
continue
if prefix == "S":
# Single-token entity
entity_text = tokens[i]
start = offsets[i]
end = start + len(entity_text)
key = f"{our_label}: {entity_text}"
spans[key].append([start, end])
i += 1
elif prefix == "B":
# Start of multi-token entity
entity_tokens = [tokens[i]]
start = offsets[i]
i += 1
while i < n:
p2, e2 = parse_tag(tags[i])
if p2 in ("I", "E") and e2 == etype:
entity_tokens.append(tokens[i])
if p2 == "E":
i += 1
break
i += 1
else:
break
entity_text = " ".join(entity_tokens)
end = start + len(entity_text)
key = f"{our_label}: {entity_text}"
spans[key].append([start, end])
else:
# Orphan I/E tag - skip
i += 1
return dict(spans)
def convert_file(path: Path, source_name: str):
"""Convert a single APTNER file to list of JSONL records."""
sentences = parse_conll_file(path)
records = []
for idx, (tokens, tags) in enumerate(sentences):
text, offsets = tokens_to_text_and_offsets(tokens)
spans = extract_spans(tokens, tags, offsets)
records.append({
"text": text,
"spans": spans,
"info": {
"id": f"{source_name}_{idx:06d}",
"source": source_name,
}
})
return records
def build_dedup_set(jsonl_path: Path):
"""Build set of text[:80] for deduplication."""
texts = set()
with open(jsonl_path) as f:
for line in f:
obj = json.loads(line)
texts.add(obj["text"][:80])
return texts
def main():
base = Path("/home/ubuntu/alkyline")
aptner_dir = base / "data" / "raw" / "APTNER"
out_dir = base / "data" / "processed"
# Load existing data for dedup
existing_train = out_dir / "enriched_5class_train_cleaned.jsonl"
existing_valid = out_dir / "enriched_5class_valid_cleaned.jsonl"
print("Building dedup set from existing data...")
dedup_set = build_dedup_set(existing_train)
dedup_valid = build_dedup_set(existing_valid)
dedup_all = dedup_set | dedup_valid
print(f" Existing unique prefixes: {len(dedup_all)}")
# Convert each split
stats = {}
for split, filename, source_name in [
("train", "APTNERtrain.txt", "aptner_train"),
("dev", "APTNERdev.txt", "aptner_dev"),
("test", "APTNERtest.txt", "aptner_test"),
]:
path = aptner_dir / filename
print(f"\nConverting {filename}...")
records = convert_file(path, source_name)
# Dedup
new_records = []
dup_count = 0
for r in records:
if r["text"][:80] in dedup_all:
dup_count += 1
else:
new_records.append(r)
# Count entities
entity_counts = defaultdict(int)
total_entities = 0
for r in new_records:
for key, positions in r["spans"].items():
label = key.split(":")[0]
entity_counts[label] += len(positions)
total_entities += len(positions)
stats[split] = {
"total": len(records),
"duplicates": dup_count,
"new": len(new_records),
"entities": total_entities,
"by_class": dict(entity_counts),
}
print(f" Total sentences: {len(records)}")
print(f" Duplicates removed: {dup_count}")
print(f" New sentences: {len(new_records)}")
print(f" Entities: {total_entities}")
print(f" By class: {dict(entity_counts)}")
# Write output
out_path = out_dir / f"aptner_5class_{split}.jsonl"
with open(out_path, "w") as f:
for r in new_records:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
print(f" Written to {out_path}")
# Summary
print("\n=== APTNER Conversion Summary ===")
for split, s in stats.items():
print(f" {split}: {s['total']} total → {s['new']} new ({s['duplicates']} dupes), {s['entities']} entities")
if __name__ == "__main__":
main()
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