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="") @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"), } ], } ], } ) 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) if annotation_file.exists(): with annotation_file.open() as f: ann_lines.extend(f.readlines()) 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