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biosses / bigbiohub.py
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gabrielaltay
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from collections import defaultdict
from dataclasses import dataclass
from enum import Enum
import logging
from pathlib import Path
from types import SimpleNamespace
from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
import datasets
if TYPE_CHECKING:
import bioc
logger = logging.getLogger(__name__)
BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
@dataclass
class BigBioConfig(datasets.BuilderConfig):
"""BuilderConfig for BigBio."""
name: str = None
version: datasets.Version = None
description: str = None
schema: str = None
subset_id: str = None
class Tasks(Enum):
NAMED_ENTITY_RECOGNITION = "NER"
NAMED_ENTITY_DISAMBIGUATION = "NED"
EVENT_EXTRACTION = "EE"
RELATION_EXTRACTION = "RE"
COREFERENCE_RESOLUTION = "COREF"
QUESTION_ANSWERING = "QA"
TEXTUAL_ENTAILMENT = "TE"
SEMANTIC_SIMILARITY = "STS"
TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
PARAPHRASING = "PARA"
TRANSLATION = "TRANSL"
SUMMARIZATION = "SUM"
TEXT_CLASSIFICATION = "TXTCLASS"
entailment_features = datasets.Features(
{
"id": datasets.Value("string"),
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
pairs_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text_1": datasets.Value("string"),
"text_2": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
qa_features = datasets.Features(
{
"id": datasets.Value("string"),
"question_id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"question": datasets.Value("string"),
"type": datasets.Value("string"),
"choices": [datasets.Value("string")],
"context": datasets.Value("string"),
"answer": datasets.Sequence(datasets.Value("string")),
}
)
text_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"labels": [datasets.Value("string")],
}
)
text2text_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text_1": datasets.Value("string"),
"text_2": datasets.Value("string"),
"text_1_name": datasets.Value("string"),
"text_2_name": datasets.Value("string"),
}
)
kb_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"passages": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"text": datasets.Sequence(datasets.Value("string")),
"offsets": datasets.Sequence([datasets.Value("int32")]),
}
],
"entities": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"text": datasets.Sequence(datasets.Value("string")),
"offsets": datasets.Sequence([datasets.Value("int32")]),
"normalized": [
{
"db_name": datasets.Value("string"),
"db_id": datasets.Value("string"),
}
],
}
],
"events": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
# refers to the text_bound_annotation of the trigger
"trigger": {
"text": datasets.Sequence(datasets.Value("string")),
"offsets": datasets.Sequence([datasets.Value("int32")]),
},
"arguments": [
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
],
}
],
"coreferences": [
{
"id": datasets.Value("string"),
"entity_ids": datasets.Sequence(datasets.Value("string")),
}
],
"relations": [
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arg1_id": datasets.Value("string"),
"arg2_id": datasets.Value("string"),
"normalized": [
{
"db_name": datasets.Value("string"),
"db_id": datasets.Value("string"),
}
],
}
],
}
)
TASK_TO_SCHEMA = {
Tasks.NAMED_ENTITY_RECOGNITION.name: "KB",
Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB",
Tasks.EVENT_EXTRACTION.name: "KB",
Tasks.RELATION_EXTRACTION.name: "KB",
Tasks.COREFERENCE_RESOLUTION.name: "KB",
Tasks.QUESTION_ANSWERING.name: "QA",
Tasks.TEXTUAL_ENTAILMENT.name: "TE",
Tasks.SEMANTIC_SIMILARITY.name: "PAIRS",
Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS",
Tasks.PARAPHRASING.name: "T2T",
Tasks.TRANSLATION.name: "T2T",
Tasks.SUMMARIZATION.name: "T2T",
Tasks.TEXT_CLASSIFICATION.name: "TEXT",
}
SCHEMA_TO_TASKS = defaultdict(set)
for task, schema in TASK_TO_SCHEMA.items():
SCHEMA_TO_TASKS[schema].add(task)
SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS)
VALID_TASKS = set(TASK_TO_SCHEMA.keys())
VALID_SCHEMAS = set(TASK_TO_SCHEMA.values())
SCHEMA_TO_FEATURES = {
"KB": kb_features,
"QA": qa_features,
"TE": entailment_features,
"T2T": text2text_features,
"TEXT": text_features,
"PAIRS": pairs_features,
}
def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
text = ann.text
if len(offsets) > 1:
i = 0
texts = []
for start, end in offsets:
chunk_len = end - start
texts.append(text[i : chunk_len + i])
i += chunk_len
while i < len(text) and text[i] == " ":
i += 1
else:
texts = [text]
return offsets, texts
def remove_prefix(a: str, prefix: str) -> str:
if a.startswith(prefix):
a = a[len(prefix) :]
return a
def parse_brat_file(
txt_file: Path,
annotation_file_suffixes: List[str] = None,
parse_notes: bool = False,
) -> Dict:
"""
Parse a brat file into the schema defined below.
`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
Will include annotator notes, when `parse_notes == True`.
brat_features = datasets.Features(
{
"id": datasets.Value("string"),
"document_id": datasets.Value("string"),
"text": datasets.Value("string"),
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
{
"offsets": datasets.Sequence([datasets.Value("int32")]),
"text": datasets.Sequence(datasets.Value("string")),
"type": datasets.Value("string"),
"id": datasets.Value("string"),
}
],
"events": [ # E line in brat
{
"trigger": datasets.Value(
"string"
), # refers to the text_bound_annotation of the trigger,
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"arguments": datasets.Sequence(
{
"role": datasets.Value("string"),
"ref_id": datasets.Value("string"),
}
),
}
],
"relations": [ # R line in brat
{
"id": datasets.Value("string"),
"head": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"tail": {
"ref_id": datasets.Value("string"),
"role": datasets.Value("string"),
},
"type": datasets.Value("string"),
}
],
"equivalences": [ # Equiv line in brat
{
"id": datasets.Value("string"),
"ref_ids": datasets.Sequence(datasets.Value("string")),
}
],
"attributes": [ # M or A lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"value": datasets.Value("string"),
}
],
"normalizations": [ # N lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"resource_name": datasets.Value(
"string"
), # Name of the resource, e.g. "Wikipedia"
"cuid": datasets.Value(
"string"
), # ID in the resource, e.g. 534366
"text": datasets.Value(
"string"
), # Human readable description/name of the entity, e.g. "Barack Obama"
}
],
### OPTIONAL: Only included when `parse_notes == True`
"notes": [ # # lines in brat
{
"id": datasets.Value("string"),
"type": datasets.Value("string"),
"ref_id": datasets.Value("string"),
"text": datasets.Value("string"),
}
],
},
)
"""
example = {}
example["document_id"] = txt_file.with_suffix("").name
with txt_file.open() as f:
example["text"] = f.read()
# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
# for event extraction
if annotation_file_suffixes is None:
annotation_file_suffixes = [".a1", ".a2", ".ann"]
if len(annotation_file_suffixes) == 0:
raise AssertionError(
"At least one suffix for the to-be-read annotation files should be given!"
)
ann_lines = []
for suffix in annotation_file_suffixes:
annotation_file = txt_file.with_suffix(suffix)
try:
with annotation_file.open() as f:
ann_lines.extend(f.readlines())
except Exception:
continue
example["text_bound_annotations"] = []
example["events"] = []
example["relations"] = []
example["equivalences"] = []
example["attributes"] = []
example["normalizations"] = []
if parse_notes:
example["notes"] = []
for line in ann_lines:
line = line.strip()
if not line:
continue
if line.startswith("T"): # Text bound
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"] = fields[1].split()[0]
ann["offsets"] = []
span_str = remove_prefix(fields[1], (ann["type"] + " "))
text = fields[2]
for span in span_str.split(";"):
start, end = span.split()
ann["offsets"].append([int(start), int(end)])
# Heuristically split text of discontiguous entities into chunks
ann["text"] = []
if len(ann["offsets"]) > 1:
i = 0
for start, end in ann["offsets"]:
chunk_len = end - start
ann["text"].append(text[i : chunk_len + i])
i += chunk_len
while i < len(text) and text[i] == " ":
i += 1
else:
ann["text"] = [text]
example["text_bound_annotations"].append(ann)
elif line.startswith("E"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
ann["arguments"] = []
for role_ref_id in fields[1].split()[1:]:
argument = {
"role": (role_ref_id.split(":"))[0],
"ref_id": (role_ref_id.split(":"))[1],
}
ann["arguments"].append(argument)
example["events"].append(ann)
elif line.startswith("R"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["type"] = fields[1].split()[0]
ann["head"] = {
"role": fields[1].split()[1].split(":")[0],
"ref_id": fields[1].split()[1].split(":")[1],
}
ann["tail"] = {
"role": fields[1].split()[2].split(":")[0],
"ref_id": fields[1].split()[2].split(":")[1],
}
example["relations"].append(ann)
# '*' seems to be the legacy way to mark equivalences,
# but I couldn't find any info on the current way
# this might have to be adapted dependent on the brat version
# of the annotation
elif line.startswith("*"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["ref_ids"] = fields[1].split()[1:]
example["equivalences"].append(ann)
elif line.startswith("A") or line.startswith("M"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
if len(info) > 2:
ann["value"] = info[2]
else:
ann["value"] = ""
example["attributes"].append(ann)
elif line.startswith("N"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["text"] = fields[2]
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
ann["resource_name"] = info[2].split(":")[0]
ann["cuid"] = info[2].split(":")[1]
example["normalizations"].append(ann)
elif parse_notes and line.startswith("#"):
ann = {}
fields = line.split("\t")
ann["id"] = fields[0]
ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
info = fields[1].split()
ann["type"] = info[0]
ann["ref_id"] = info[1]
example["notes"].append(ann)
return example
def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
"""
Transform a brat parse (conforming to the standard brat schema) obtained with
`parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
:param brat_parse:
"""
unified_example = {}
# Prefix all ids with document id to ensure global uniqueness,
# because brat ids are only unique within their document
id_prefix = brat_parse["document_id"] + "_"
# identical
unified_example["document_id"] = brat_parse["document_id"]
unified_example["passages"] = [
{
"id": id_prefix + "_text",
"type": "abstract",
"text": [brat_parse["text"]],
"offsets": [[0, len(brat_parse["text"])]],
}
]
# get normalizations
ref_id_to_normalizations = defaultdict(list)
for normalization in brat_parse["normalizations"]:
ref_id_to_normalizations[normalization["ref_id"]].append(
{
"db_name": normalization["resource_name"],
"db_id": normalization["cuid"],
}
)
# separate entities and event triggers
unified_example["events"] = []
non_event_ann = brat_parse["text_bound_annotations"].copy()
for event in brat_parse["events"]:
event = event.copy()
event["id"] = id_prefix + event["id"]
trigger = next(
tr
for tr in brat_parse["text_bound_annotations"]
if tr["id"] == event["trigger"]
)
if trigger in non_event_ann:
non_event_ann.remove(trigger)
event["trigger"] = {
"text": trigger["text"].copy(),
"offsets": trigger["offsets"].copy(),
}
for argument in event["arguments"]:
argument["ref_id"] = id_prefix + argument["ref_id"]
unified_example["events"].append(event)
unified_example["entities"] = []
anno_ids = [ref_id["id"] for ref_id in non_event_ann]
for ann in non_event_ann:
entity_ann = ann.copy()
entity_ann["id"] = id_prefix + entity_ann["id"]
entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
unified_example["entities"].append(entity_ann)
# massage relations
unified_example["relations"] = []
skipped_relations = set()
for ann in brat_parse["relations"]:
if (
ann["head"]["ref_id"] not in anno_ids
or ann["tail"]["ref_id"] not in anno_ids
):
skipped_relations.add(ann["id"])
continue
unified_example["relations"].append(
{
"arg1_id": id_prefix + ann["head"]["ref_id"],
"arg2_id": id_prefix + ann["tail"]["ref_id"],
"id": id_prefix + ann["id"],
"type": ann["type"],
"normalized": [],
}
)
if len(skipped_relations) > 0:
example_id = brat_parse["document_id"]
logger.info(
f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
f" Skip (for now): "
f"{list(skipped_relations)}"
)
# get coreferences
unified_example["coreferences"] = []
for i, ann in enumerate(brat_parse["equivalences"], start=1):
is_entity_cluster = True
for ref_id in ann["ref_ids"]:
if not ref_id.startswith("T"): # not textbound -> no entity
is_entity_cluster = False
elif ref_id not in anno_ids: # event trigger -> no entity
is_entity_cluster = False
if is_entity_cluster:
entity_ids = [id_prefix + i for i in ann["ref_ids"]]
unified_example["coreferences"].append(
{"id": id_prefix + str(i), "entity_ids": entity_ids}
)
return unified_example