File size: 37,386 Bytes
b11ac48 b8ce02e b11ac48 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 |
import io
import json
import os
import sys
import argparse
import re
import tarfile
from collections import defaultdict
import dataclasses
from datetime import datetime
from typing import Any, Dict, List, Tuple, Optional
import pandas as pd
import spacy
from nltk.corpus import framenet as fn
from nltk.corpus.reader.framenet import FramenetError
from spacy.tokens import Token
from sociofillmore.crashes.utils import is_a_dutch_text
ITALIAN_ACTIVE_AUX = ["avere", "ha", "ho", "hai", "avete", "hanno", "abbiamo"]
DUTCH_ACTIVE_AUX = ["heb", "hebben", "heeft"]
active_frames_df = pd.read_csv("resources/active_frames_full.csv")
ACTIVE_FRAMES = active_frames_df[active_frames_df["active"]]["frame"].tolist()
IGNORE_DEP_LABELS = ["punct"]
DEEP_FRAMES = [
"Transitive_action",
"Causation",
"Transition_to_a_state",
"Event",
"State",
]
# SYNTAX_ANALYSIS_CACHE_FILES = {
# "femicides/rai": "resources/rai_syntax_analysis_cache.json",
# "femicides/rai_main": "resources/rai_main_syntax_analysis_cache.json",
# "femicides/olv": "resources/olv_syntax_analysis_cache.json",
# "crashes/thecrashes": "resources/thecrashes_syntax_analysis_cache.json",
# "migration/pavia": "resources/migration_pavia_syntax_analysis_cache.json"
# }
SYNTAX_ANALYSIS_CACHE_FILES = {
"femicides/rai": "output/femicides/syntax_cache/rai_ALL",
"femicides/rai_main": "output/femicides/syntax_cache/rai_main",
"femicides/rai_ALL": "output/femicides/syntax_cache/rai_ALL",
"femicides/olv": "output/femicides/syntax_cache/olv",
"crashes/thecrashes": "output/crashes/syntax_cache/thecrashes",
"migration/pavia": "output/migration/syntax_cache/pavia",
}
DEEP_FRAMES_CACHE_FILE = "resources/deep_frame_cache.json"
DEP_LABEL_CACHE_FILE = "resources/dep_labels.txt"
POSSIBLE_CONSTRUCTIONS = [
"nonverbal",
"verbal:active",
"verbal:impersonal",
"verbal:reflexive",
"verbal:passive",
"verbal:unaccusative",
"other",
]
def load_deep_frames_cache():
if os.path.isfile(DEEP_FRAMES_CACHE_FILE):
print("Loading deep frame cache...")
with open(DEEP_FRAMES_CACHE_FILE, encoding="utf-8") as f:
deep_frames_cache = json.load(f)
else:
deep_frames_cache = {}
return deep_frames_cache
# make spacy work with google app engine
# (see https://stackoverflow.com/questions/55228492/spacy-on-gae-standard-second-python-exceeds-memory-of-largest-instance)
# nlp = spacy.load("it_core_news_md")
nlp = None
@dataclasses.dataclass
class AnnotationSpan:
tokens_idx: List[int]
tokens_str: List[str]
@dataclasses.dataclass
class FrameStructure:
frame: str
deep_frame: str
target: Optional[AnnotationSpan]
roles: List[Tuple[str, AnnotationSpan]]
deep_roles: List[Tuple[str, AnnotationSpan]]
def make_syntax_cache(dataset, skip_fn=None):
print(f"make_syntax_cache({dataset})")
if dataset == "femicides/rai":
corpus_tarball = "output/femicides/lome/lome_0shot/multilabel_rai_blocks"
corpus = "rai"
spacy_model = "it_core_news_md"
elif dataset == "femicides/rai_main":
corpus_tarball = "output/femicides/lome/lome_0shot/multilabel_rai_main_blocks"
corpus = "rai_main"
spacy_model = "it_core_news_md"
elif dataset == "femicides/rai_ALL":
corpus_tarball = "output/femicides/lome/lome_0shot/multilabel_rai_ALL_blocks"
corpus = "rai_ALL"
spacy_model = "it_core_news_md"
elif dataset == "femicides/olv":
corpus_tarball = "output/femicides/lome/lome_0shot/multilabel_olv_blocks"
corpus = "olv"
spacy_model = "it_core_news_md"
elif dataset == "crashes/thecrashes":
corpus_tarball = "output/crashes/lome/lome_0shot/multilabel_thecrashes_blocks"
corpus = "thecrashes"
spacy_model = "nl_core_news_md"
elif dataset == "migration/pavia":
corpus_tarball = "output/migration/lome/lome_0shot/multilabel_pavia_blocks"
# corpus_tarball = "output/migration/lome/lome_zs-tgt_ev-frm/multilabel_pavia.tar.gz"
corpus = "pavia"
spacy_model = "it_core_news_md"
else:
raise ValueError("Unsupported dataset!")
print("params:")
print(f"\tcorpus_tarball: {corpus_tarball}")
print(f"\tcorpus: {corpus}")
print(f"\tspacy: {spacy_model}")
print("processing files...")
for block in os.listdir(corpus_tarball):
print(block)
with tarfile.open(os.path.join(corpus_tarball, block)) as tar_in:
# check if output tarball exists
cache_location = SYNTAX_ANALYSIS_CACHE_FILES[dataset]
if not os.path.isdir(cache_location):
os.makedirs(cache_location)
lome_files = [f for f in tar_in.getmembers(
) if f.name.endswith(".comm.json")]
lome_files.sort(key=lambda file: file.name)
for file in lome_files:
print(f"\tprocessing file {file}")
doc_id = re.search(r"lome_(\d+)\.comm\.json", file.name).group(1)
skipped = False
if skip_fn is not None:
if skip_fn(doc_id):
print(f"\t\tskip_fn: skipping file {file}")
skipped = True
if skipped:
syntax_analyses = None
else:
file_obj = io.TextIOWrapper(tar_in.extractfile(file))
annotations = json.load(file_obj)
syntax_analyses = []
for sentence in annotations:
syntax_analyses.append(
syntax_analyze(sentence, spacy_model))
# use last two chars of filename as key
file_key = doc_id[:2]
cache_file = f"{cache_location}/{file_key}.json"
if os.path.isfile(cache_file):
with open(cache_file, encoding="utf-8") as f:
key_cache = json.load(f)
else:
key_cache = {}
key_cache[doc_id] = syntax_analyses
with open(cache_file, "w", encoding="utf-8") as f:
json.dump(key_cache, f)
def make_syntax_cache_key(filename):
doc_id = re.search(r"/\d+/lome_(\d+)\.comm\.json", filename).group(1)
return doc_id
def clean_sentence_(sentence):
idx_to_remove = []
for i, tok in enumerate(sentence["tokens"]):
# remove whitespace tokens
if not tok.strip():
idx_to_remove.append(i)
idx_to_remove.reverse()
for idx in idx_to_remove:
for annotation_list in sentence.values():
annotation_list.pop(idx)
def process_prediction_file(
filename: str,
dataset_name: str,
syntax_cache: str,
deep_frames_cache: dict,
tmp_cache: Optional[dict] = None,
file_obj: io.TextIOBase = None,
syntax_cache_key: Optional[str] = None,
deep_frames_list: Optional[List[str]] = None,
spacy_model: str = "it_core_news_md",
spacy_model_obj = None
) -> Tuple[List, ...]:
"""
Process a predictions JSON file
:param filename: path to the JSON file
:param syntax_cache: see `make_syntax_cache()`
:param spacy model: spacy model to be used for syntactic analysis
:param file_obj: already opened object corresponding to `filename`. If given, `file_obj` will be used instead
of loading it from `filename`. This is useful when reading the entire corpus from a tarball (which is what the
SocioFillmore webapp does)
:return:
"""
print("Processing", filename)
if file_obj is not None:
annotations = json.load(file_obj)
else:
with open(filename, encoding="utf-8") as f:
annotations = json.load(f)
if syntax_cache is None:
syntax_analyses = []
for sentence in annotations:
syntax_analyses.append(syntax_analyze(sentence, spacy_model, spacy_model_obj))
else:
if syntax_cache_key is None:
syntax_cache_key = make_syntax_cache_key(filename)
if tmp_cache is not None and syntax_cache_key in tmp_cache:
syntax_analyses = tmp_cache[syntax_cache_key]
else:
with open(f"{syntax_cache}/{syntax_cache_key[:2]}.json", encoding="utf-8") as cache_file:
grouped_analyses = json.load(cache_file)
syntax_analyses = grouped_analyses[syntax_cache_key]
if tmp_cache is not None:
tmp_cache.clear()
tmp_cache.update(grouped_analyses)
fn_structures: List[Dict[int, FrameStructure]] = []
sentences: List[List[str]] = []
role_analyses: List[Dict[int, Dict[str, str]]] = []
for sent_idx, sentence in enumerate(annotations):
clean_sentence_(sentence)
try:
sent_structures = process_fn_sentence(
sentence, deep_frames_cache, deep_frames_list=deep_frames_list
)
# seems to occur for one specific file in the migration set, TODO find out what happens
except AttributeError:
print("Error processing FN annotations")
sent_structures = {}
syntax = syntax_analyses[sent_idx]
# disambiguate syntactic constructions
for fs in sent_structures.values():
target_idx = str(fs.target.tokens_idx[0])
if target_idx not in syntax:
print(
f"Prediction file {filename}: Cannot find syntactic information for target at idx={target_idx}")
continue
fs_syn = syntax[target_idx][-1]
disambiguate_cxs_(fs, fs_syn)
roles = process_syn_sem_roles(sent_structures, syntax)
role_analyses.append(roles)
sentences.append(sentence["tokens"])
fn_structures.append(sent_structures)
return sentences, fn_structures, syntax_analyses, role_analyses
def disambiguate_cxs_(struct: FrameStructure, tgt_syntax):
# no "_" at the beginning: no disambiguation needed
cx = tgt_syntax["syn_construction"]
if not cx.startswith("_"):
return
# print(struct.frame, struct.deep_frame)
# NB works only for the selected relevant frames! if any other frames are added, make sure to update this
if struct.deep_frame in ["Transitive_action", "Causation", "Emotion_directed", "Quarreling", "Impact", "Committing_crime"]:
frame_agentivity_type = "active"
elif struct.frame in ACTIVE_FRAMES:
frame_agentivity_type = "active"
elif struct.frame == "Event":
frame_agentivity_type = "impersonal"
else:
frame_agentivity_type = "unaccusative"
if cx == "_verbal:ACTIVE":
new_cx = f"verbal:{frame_agentivity_type}"
elif cx in ["_verbal:ADPOS", "_verbal:OTH_PART"]:
if frame_agentivity_type == "active":
new_cx = "verbal:passive"
else:
new_cx = f"verbal:{frame_agentivity_type}"
else:
raise ValueError(f"Unknown construction placeholder {cx}")
tgt_syntax["syn_construction"] = new_cx
def find_governed_roles(
syn_self: Dict[str, Any],
syn_children: List[Dict[str, Any]],
roles: List[Tuple[str, AnnotationSpan]],
) -> Dict[str, str]:
roles_found = {}
# find roles that are governed by the predicate
for node in [syn_self] + syn_children:
for role_name, role_span in roles:
if node["lome_idx"] in role_span.tokens_idx:
dep_label = node["dependency"]
if role_name not in roles_found and dep_label not in IGNORE_DEP_LABELS:
if node == syn_self:
roles_found[role_name] = None
else:
roles_found[role_name] = dep_label + "↓"
return roles_found
def analyze_role_dependencies(
fn_struct,
syntax,
role_analysis=None,
tgt_idx=None,
min_depth=-10,
max_depth=10,
depth=0,
label_prefix="",
):
if role_analysis is None:
role_analysis = {}
if tgt_idx is None:
tgt_idx = fn_struct.target.tokens_idx[0]
if depth > max_depth:
return role_analysis
if depth < min_depth:
return role_analysis
new_analysis = {}
new_analysis.update(role_analysis)
token_syntax = syntax[str(tgt_idx)][0]
def update_analysis(mapping):
for role, dep in mapping.items():
if role not in new_analysis:
if label_prefix:
if dep is None:
label = label_prefix
depth_label = depth
else:
label = label_prefix + "--" + dep
depth_label = depth + 1 if depth > 0 else depth - 1
else:
if dep is None:
label = "⋆"
depth_label = depth
else:
label = dep
depth_label = depth + 1 if depth > 0 else depth - 1
new_analysis[role] = label, depth_label
update_analysis(
find_governed_roles(
token_syntax, token_syntax["children"], fn_struct.roles)
)
# from the initial predicate: first try the children
if depth <= 0:
for child in token_syntax["children"]:
child_analysis = analyze_role_dependencies(
fn_struct,
syntax,
role_analysis=new_analysis,
tgt_idx=child["lome_idx"],
max_depth=max_depth,
min_depth=min_depth,
depth=depth - 1,
label_prefix=child["dependency"] + "↓"
)
new_analysis.update(child_analysis)
# ... then try the ancestors
if depth >= 0:
if not token_syntax["ancestors"]:
return new_analysis
first_ancestor = token_syntax["ancestors"][0]
return analyze_role_dependencies(
fn_struct,
syntax,
role_analysis=new_analysis,
tgt_idx=first_ancestor["lome_idx"],
max_depth=max_depth,
min_depth=min_depth,
depth=depth + 1,
label_prefix=token_syntax["dependency"] + "↑",
)
else:
return new_analysis
def process_syn_sem_roles(
sent_structures: Dict[int, FrameStructure], syntax: Dict[str, List[Dict[str, Any]]]
) -> Dict[int, Dict[str, str]]:
analyses = defaultdict(dict)
# go through all frame targets
for struct in sent_structures.values():
tgt_idx = struct.target.tokens_idx[0]
role_deps = analyze_role_dependencies(struct, syntax, max_depth=10)
analyses[tgt_idx] = clean_role_deps(role_deps)
return analyses
def clean_role_deps(role_deps):
res = {}
for role, (dep_str, depth) in role_deps.items():
dep_parts = dep_str.split("--")
if len(dep_parts) == 1:
res[role] = dep_str, depth
else:
res[role] = "--".join([dp[-1]
for dp in dep_parts[:-1]] + [dep_parts[-1]]), depth
return res
def map_or_lookup_deep_frame(
frame: str, deep_frames_cache, save_modified_cache=False, deep_frames_list=None
) -> Tuple[str, Dict[str, str]]:
if frame in deep_frames_cache:
return deep_frames_cache[frame]
else:
deep_frame, mapping = map_to_deep_frame(
frame, deep_frames_list=deep_frames_list
)
deep_frames_cache[frame] = [deep_frame, mapping]
if save_modified_cache:
with open(DEEP_FRAMES_CACHE_FILE, "w", encoding="utf-8") as f:
json.dump(deep_frames_cache, f)
return deep_frames_cache[frame]
def map_to_deep_frame(
frame: str,
target: Optional[str] = None,
mapping: Optional[Dict[str, str]] = None,
self_mapping: Optional[Dict[str, str]] = None,
deep_frames_list: Optional[List[str]] = None,
) -> Tuple[str, Dict[str, str]]:
if deep_frames_list is None:
deep_frames_list = DEEP_FRAMES
# look up in FrameNet
try:
fn_entry = fn.frame(frame)
except FramenetError:
return frame, {}
except LookupError:
return frame, {}
# initial call: `target` == `frame`, mapping maps to self
if target is None:
target = frame
if mapping is None or self_mapping is None:
mapping = self_mapping = {role: role for role in fn_entry.FE.keys()}
# base case: our frame is a deep frame
if frame in deep_frames_list:
return frame, mapping
# otherwise, look at parents
inh_relations = [
fr
for fr in fn_entry.frameRelations
if fr.type.name == "Inheritance" and fr.Child == fn_entry
]
parents = [fr.Parent for fr in inh_relations]
# no parents --> failure, return original frame
if not inh_relations:
return target, self_mapping
# one parent: follow that parent
if len(inh_relations) == 1:
parent_rel = inh_relations[0]
parent = parents[0]
new_mapping = define_fe_mapping(mapping, parent_rel)
return map_to_deep_frame(
parent.name, target, new_mapping, self_mapping, deep_frames_list
)
# more parents: check if any of them leads to a deep frame
deep_frames = []
deep_mappings = []
for parent_rel, parent in zip(inh_relations, parents):
new_mapping = define_fe_mapping(mapping, parent_rel)
final_frame, final_mapping = map_to_deep_frame(
parent.name, target, new_mapping, self_mapping, deep_frames_list
)
if final_frame in deep_frames_list:
deep_frames.append(final_frame)
deep_mappings.append(final_mapping)
for deep_frame in deep_frames_list:
if deep_frame in deep_frames:
idx = deep_frames.index(deep_frame)
return deep_frame, deep_mappings[idx]
# nothing found, return original frame
return target, self_mapping
def define_fe_mapping(mapping, parent_rel):
child_to_parent_mapping = {
fer.subFEName: fer.superFEName for fer in parent_rel.feRelations
}
target_to_parent_mapping = {
role: child_to_parent_mapping[mapping[role]]
for role in mapping
if mapping[role] in child_to_parent_mapping
}
return target_to_parent_mapping
def is_at_root(syntax_info):
# you should either be the actual root...
if syntax_info["dependency"] == "ROOT":
return True
# ... or be the subject of the root
if syntax_info["dependency"] == "nsubj" and syntax_info["ancestors"][0]["dependency"] == "ROOT":
return True
return False
def get_tarball_blocks(dataset, lome_model="lome_0shot"):
if dataset == "femicides/rai":
return f"output/femicides/lome/{lome_model}/multilabel_rai_ALL_blocks"
if dataset == "femicides/rai_main":
return f"output/femicides/lome/{lome_model}/multilabel_rai_main_blocks"
elif dataset == "femicides/olv":
return f"output/femicides/lome/{lome_model}/multilabel_olv_blocks"
elif dataset == "crashes/thecrashes":
return f"output/crashes/lome/{lome_model}/multilabel_thecrashes_blocks"
elif dataset == "migration/pavia":
return f"output/migration/lome/{lome_model}/multilabel_pavia_blocks"
else:
raise ValueError("Unsupported dataset!")
def analyze_single_document(doc_id, event_id, lome_model, dataset, texts_df, deep_frames_cache):
data_domain, data_corpus = dataset.split("/")
syntax_cache = SYNTAX_ANALYSIS_CACHE_FILES[dataset]
print(dataset)
if dataset == "migration/pavia": # this is a hack, fix it!
pred_file_path = f"output/migration/lome/multilabel/{lome_model}/pavia/{event_id}/lome_{doc_id}.comm.json"
elif dataset == "femicides/olv":
pred_file_path = f"output/femicides/lome/lome_0shot/multilabel/olv/{event_id}/lome_{doc_id}.comm.json"
else:
pred_file_path = f"output/{data_domain}/lome/lome_0shot/multilabel/{data_corpus}/{event_id}/lome_{doc_id}.comm.json"
print(f"Analyzing file {pred_file_path}")
doc_id = os.path.basename(pred_file_path).split(".")[0].split("_")[1]
doc_key = doc_id[:2]
tarball = get_tarball_blocks(dataset, lome_model) + f"/block_{doc_key}.tar"
with tarfile.open(tarball, "r") as tar_f:
pred_file = io.TextIOWrapper(tar_f.extractfile(pred_file_path))
(
sents,
pred_structures,
syntax_analyses,
role_analyses,
) = process_prediction_file(
filename=pred_file_path,
dataset_name=dataset,
file_obj=pred_file,
syntax_cache=syntax_cache,
deep_frames_cache=deep_frames_cache
)
output = []
for sent, structs, syntax, roles in zip(
sents, pred_structures, syntax_analyses, role_analyses
):
output.append(
{
"sentence": sent,
"fn_structures": [
dataclasses.asdict(fs) for fs in structs.values()
],
"syntax": syntax,
"roles": roles,
"meta": {
"event_id": event_id,
"doc_id": doc_id,
"text_meta": get_text_meta(doc_id, texts_df),
},
}
)
return output
def get_text_meta(doc_id, texts_df):
row = texts_df[texts_df["text_id"] == int(doc_id)].iloc[0]
if "pubdate" in row:
pubdate = row["pubdate"] if not pd.isna(row["pubdate"]) else None
elif "pubyear" in row:
pubdate = int(row["pubyear"])
else:
pubdate = None
return {
"url": row["url"] if "url" in row else None,
"pubdate": pubdate,
"provider": row["provider"],
"title": row["title"] if not pd.isna(row["title"]) else None,
"days_after_event": int(row["days_after_event"]) if "days_after_event" in row and not pd.isna(row["days_after_event"]) else 0
}
def process_fn_sentence(
sentence, deep_frames_cache, post_process=True, deep_frames_list=None
):
# frame structures in the sentence
sent_structures: Dict[int, FrameStructure] = {}
# role spans currently being built up (per structure + role name)
cur_spans: Dict[Tuple[int, str]] = {}
for token_idx, (token_str, frame_annos) in enumerate(
zip(sentence["tokens"], sentence["frame_list"])
):
for fa in frame_annos:
# remove "virtual root" nonsense token
if "@@VIRTUAL_ROOT@@" in fa:
continue
fa = fa.split("@@")[0] # remove confidence score if it's there
anno, struct_id_str = fa.split("@")
struct_id = int(struct_id_str)
frame_name = anno.split(":")[1]
deep_frame, deep_frame_mapping = map_or_lookup_deep_frame(
frame_name, deep_frames_cache, deep_frames_list=deep_frames_list
)
if struct_id not in sent_structures:
sent_structures[struct_id] = FrameStructure(
frame=frame_name,
deep_frame=deep_frame,
target=None,
roles=[],
deep_roles=[],
)
cur_struct = sent_structures[struct_id]
# TODO: get rid of this hack
anno = anno.replace("I::", "I:")
anno = anno.replace("B::", "B:")
if anno.split(":")[0] == "T":
if cur_struct.target is None:
cur_struct.target = AnnotationSpan(
[token_idx], [token_str])
else:
cur_struct.target.tokens_idx.append(token_idx)
cur_struct.target.tokens_str.append(token_str)
elif anno.split(":")[0] == "B":
role_name = anno.split(":")[2]
role_span = AnnotationSpan([token_idx], [token_str])
cur_struct.roles.append((role_name, role_span))
if role_name in deep_frame_mapping:
cur_struct.deep_roles.append(
(deep_frame_mapping[role_name], role_span)
)
cur_spans[(struct_id, role_name)] = role_span
elif anno.split(":")[0] == "I":
role_name = anno.split(":")[2]
role_span = cur_spans[(struct_id, role_name)]
role_span.tokens_str.append(token_str)
role_span.tokens_idx.append(token_idx)
# post-process: remove punctuation in targets
if post_process:
for fs in sent_structures.values():
if len(fs.target.tokens_str) > 1:
target_tok_str_to_remove = []
target_tok_idx_to_remove = []
for tok_str, tok_idx in zip(fs.target.tokens_str, fs.target.tokens_idx):
if tok_str in ["``", "''", "`", "'", ".", ",", ";", ":"]:
target_tok_str_to_remove.append(tok_str)
target_tok_idx_to_remove.append(tok_idx)
for tok_str, tok_idx in zip(
target_tok_str_to_remove, target_tok_idx_to_remove
):
fs.target.tokens_str.remove(tok_str)
fs.target.tokens_idx.remove(tok_idx)
return sent_structures
def map_back_spacy_lome_tokens(spacy_doc, lome_tokens):
if len(lome_tokens) > len(spacy_doc):
raise ValueError(
f"Cannot re-tokenize (#lome={len(lome_tokens)} // #spacy={len(spacy_doc)})"
)
spacy_to_lome = {}
lome_idx = 0
for spacy_idx, spacy_token in enumerate(spacy_doc):
spacy_to_lome[spacy_idx] = lome_idx
# whitespace after token: tokens correspond
if spacy_token.whitespace_:
lome_idx += 1
return spacy_to_lome
def get_syn_category(spacy_token):
if spacy_token.pos_ == "NOUN":
return "n"
if spacy_token.pos_ == "ADJ":
return "adj"
if spacy_token.pos_ == "ADV":
return "adv"
if spacy_token.pos_ == "ADP":
return "p"
if spacy_token.pos_ == "VERB":
if spacy_token.morph.get("VerbForm") == ["Fin"]:
return "v:fin"
if spacy_token.morph.get("VerbForm") == ["Part"]:
return "v:part"
if spacy_token.morph.get("VerbForm") == ["Ger"]:
return "v:ger"
if spacy_token.morph.get("VerbForm") == ["Inf"]:
return "v:inf"
return "other"
def syntax_analyze(sentence, spacy_model_name, spacy_model_obj=None) -> Dict[str, Dict[str, Any]]:
lome_tokens = sentence["tokens"]
# load spacy model locally (so that it works in GAE)
# global nlp
if spacy_model_obj is not None:
nlp = spacy_model_obj
else:
nlp = spacy.load(spacy_model_name)
spacy_doc = nlp(" ".join(lome_tokens))
analysis = defaultdict(list)
spacy_to_lome_tokens = map_back_spacy_lome_tokens(spacy_doc, lome_tokens)
for spacy_idx, token in enumerate(spacy_doc):
lome_idx = spacy_to_lome_tokens[spacy_idx]
syn_category = get_syn_category(token)
syn_construction = get_syn_construction(token, syn_category)
children = []
for c in token.children:
children.append(
{
"token": c.text,
"spacy_idx": c.i,
"lome_idx": spacy_to_lome_tokens[c.i],
"syn_category": get_syn_category(c),
"dependency": c.dep_,
}
)
ancestors = []
for a in token.ancestors:
ancestors.append(
{
"token": a.text,
"spacy_idx": a.i,
"lome_idx": spacy_to_lome_tokens[a.i],
"syn_category": get_syn_category(a),
"dependency": a.dep_,
}
)
# str key so that it doesn't change when converting to JSON
lome_key = str(lome_idx)
analysis[lome_key].append(
{
"token": token.text,
"dependency": token.dep_,
"spacy_idx": spacy_idx,
"lome_idx": lome_idx,
"syn_category": syn_category,
"syn_construction": syn_construction,
"children": children,
"ancestors": ancestors,
}
)
return analysis
def get_syn_construction(token: Token, syn_category: str) -> str:
if syn_category in ["n", "adj", "adv", "p"]:
return "nonverbal"
if syn_category.startswith("v:"):
# find reflexives
for c in token.children:
if c.lemma_.lower() in ["si", "zich", "zichzelf"]:
return "verbal:reflexive"
# find impersonal constructions
for c in token.children:
if c.dep_ == "expl":
return "verbal:impersonal"
# all other finite verbs/gerunds/infinites -> active construction
if syn_category in ["v:fin", "v:ger", "v:inf"]:
return "_verbal:ACTIVE"
if syn_category == "v:part":
if token.dep_ == "acl":
return "_verbal:ADPOS"
for c in token.children:
# passive subj or auxiliary present: it's a passive
if c.dep_ in ["nsubj:pass", "aux:pass"]:
return "verbal:passive"
# auxiliary "HAVE" (avere/hebben) present: it's an active
if (
c.dep_ == "aux"
and c.lemma_.lower() in ITALIAN_ACTIVE_AUX + DUTCH_ACTIVE_AUX
):
return "verbal:active"
return "_verbal:OTH_PART"
return "other"
def get_syntax_info(struct: FrameStructure, syntax: Dict) -> Dict:
target_idx = str(struct.target.tokens_idx[0])
# print(target_idx, syntax)
syntax_for_target = syntax[target_idx]
return syntax_for_target[-1]
def enrich_texts_df(texts_df: pd.DataFrame, events_df: pd.DataFrame):
time_delta_rows: List[Optional[int]] = []
for idx, text_row in texts_df.iterrows():
try:
event_row = events_df[events_df["event:id"]
== text_row["event_id"]].iloc[0]
except IndexError:
print(f"Skipping {idx} (IndexError)")
time_delta_rows.append(None)
if "pubdate" not in text_row or pd.isna(text_row["pubdate"]) or pd.isna(event_row["event:date"]):
time_delta_rows.append(None)
else:
try:
pub_date = datetime.strptime(
text_row["pubdate"], "%Y-%m-%d %H:%M:%S")
event_date = datetime.strptime(
event_row["event:date"], "%Y-%m-%d")
time_delta = pub_date - event_date
time_delta_days = time_delta.days
time_delta_rows.append(time_delta_days)
except ValueError as e:
print(
f"\t\terror parsing dates, see below for more info:\n\t\t{e}")
time_delta_rows.append(None)
return texts_df.assign(days_after_event=time_delta_rows)
def read_frames_of_interest(dataset) -> List[str]:
if dataset in ["femicides/rai", "femicides/olv"]:
file = "resources/femicide_frame_list.txt"
elif dataset == "crashes/thecrashes":
file = "resources/crashes_frame_list.txt"
elif dataset == "migration/pavia":
file = "resources/migration_frame_list.txt"
else:
raise ValueError("Unsupported dataset")
frames = set()
with open(file, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line.startswith("#") or not line:
continue
frames.add(line[0].upper() + line[1:].lower())
return sorted(frames)
def make_dep_label_cache():
labels = set()
for dataset in ["femicides/rai", "crashes/thecrashes", "migration/pavia"]:
tarball = (
"output/femicides/lome/lome_0shot/multilabel_rai.tar.gz"
if dataset == "femicides/rai"
else "output/crashes/lome/lome_0shot/multilabel_thecrashes.tar.gz"
if dataset == "crashes/thecrashes"
else "output/migration/lome/lome_0shot/multilabel_pavia.tar.gz"
)
spacy_model = (
"it_core_news_md" if dataset["femicides/rai",
"migration/pavia"] else "nl_core_news_md"
)
deep_frames_cache = load_deep_frames_cache(dataset)
syntax_cache = SYNTAX_ANALYSIS_CACHE_FILES[dataset]
with tarfile.open(tarball, "r:gz") as tar_f:
for mem in [
m.name for m in tar_f.getmembers() if m.name.endswith(".comm.json")
]:
if mem is None:
continue
print(mem)
mem_obj = io.TextIOWrapper(tar_f.extractfile(mem))
(_, _, _, role_analyses,) = process_prediction_file(
filename=mem,
dataset_name=dataset,
file_obj=mem_obj,
syntax_cache=syntax_cache,
deep_frames_cache=deep_frames_cache,
spacy_model=spacy_model,
)
if role_analyses is None:
print(f"\tSkipping file {mem}, no role analyses found")
continue
for sent_ra in role_analyses:
for ra in sent_ra.values():
for dep, _ in ra.values():
labels.add(dep)
with open(DEP_LABEL_CACHE_FILE, "w", encoding="utf-8") as f_out:
for label in sorted(labels):
f_out.write(label + os.linesep)
def analyze_external_file(file_in, file_out, spacy_model):
deep_frames_cache = load_deep_frames_cache()
(
sents,
pred_structures,
syntax_analyses,
role_analyses,
) = process_prediction_file(file_in, "", None, deep_frames_cache, spacy_model_obj=spacy_model)
output = []
for sent, structs, syntax, roles in zip(
sents, pred_structures, syntax_analyses, role_analyses
):
output.append(
{
"sentence": sent,
"fn_structures": [
dataclasses.asdict(fs) for fs in structs.values()
],
"syntax": syntax,
"roles": roles
}
)
with open(file_out, "w", encoding="utf-8") as f_out:
json.dump(output, f_out, indent=4)
if __name__ == "__main__":
ap = argparse.ArgumentParser()
ap.add_argument("command", choices=[
"make_syntax_cache", "make_dep_label_cache", "analyze_file"
])
ap.add_argument("dataset", choices=["femicides/rai", "femicides/rai_main", "femicides/rai_ALL",
"femicides/olv", "crashes/thecrashes", "migration/pavia", "*"])
ap.add_argument("--input_file", type=str, default="")
ap.add_argument("--output_file", type=str, default="")
args = ap.parse_args()
if args.command == "make_syntax_cache":
if args.dataset == "*":
raise ValueError(
"Please specificy a dataset for `make_syntax_cache`")
if args.dataset == "crashes/thecrashes":
make_syntax_cache(
"crashes/thecrashes", skip_fn=lambda f: not is_a_dutch_text(f)
)
elif args.dataset == "femicides/rai":
make_syntax_cache("femicides/rai")
elif args.dataset == "femicides/rai_main":
make_syntax_cache("femicides/rai_main")
elif args.dataset == "femicides/rai_ALL":
make_syntax_cache("femicides/rai_ALL")
elif args.dataset == "femicides/olv":
make_syntax_cache("femicides/olv")
else:
make_syntax_cache("migration/pavia")
elif args.command == "make_dep_label_cache":
make_dep_label_cache()
elif args.command == "analyze_file":
analyze_external_file(args.input_file, args.output_file)
|