import dataclasses import logging from typing import Union from pie_core import AnnotationLayer, Document, annotation_field from pie_documents.document.processing import ( RegexPartitioner, RelationArgumentSorter, SpansViaRelationMerger, TextSpanTrimmer, ) from pie_documents.documents import ( TextDocumentWithLabeledMultiSpansAndBinaryRelations, TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansAndBinaryRelations, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions, ) from pie_datasets.builders import BratBuilder, BratConfig from pie_datasets.builders.brat import ( BratAttribute, BratDocument, BratDocumentWithMergedSpans, BratNote, ) from pie_datasets.core.dataset import DocumentConvertersType from pie_datasets.document.processing import Caster, Pipeline logger = logging.getLogger(__name__) URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip" SPLIT_PATHS = {"train": "compiled_corpus"} @dataclasses.dataclass class ConvertedBratDocument(TextDocumentWithLabeledMultiSpansAndBinaryRelations): span_attributes: AnnotationLayer[BratAttribute] = annotation_field( target="labeled_multi_spans" ) relation_attributes: AnnotationLayer[BratAttribute] = annotation_field( target="binary_relations" ) notes: AnnotationLayer[BratNote] = annotation_field( targets=[ "labeled_multi_spans", "binary_relations", "span_attributes", "relation_attributes", ] ) @dataclasses.dataclass class ConvertedBratDocumentWithMergedSpans(TextDocumentWithLabeledSpansAndBinaryRelations): span_attributes: AnnotationLayer[BratAttribute] = annotation_field(target="labeled_spans") relation_attributes: AnnotationLayer[BratAttribute] = annotation_field( target="binary_relations" ) notes: AnnotationLayer[BratNote] = annotation_field( targets=["labeled_spans", "binary_relations", "span_attributes", "relation_attributes"] ) def get_common_converter_pipeline_steps(target_document_type: type[Document]) -> dict: return dict( cast=Caster( document_type=target_document_type, field_mapping={"spans": "labeled_spans", "relations": "binary_relations"}, ), trim_adus=TextSpanTrimmer(layer="labeled_spans"), sort_symmetric_relation_arguments=RelationArgumentSorter( relation_layer="binary_relations", label_whitelist=["parts_of_same", "semantically_same", "contradicts"], ), ) def get_common_converter_pipeline_steps_with_resolve_parts_of_same( target_document_type: type[Document], ) -> dict: return dict( cast=Caster( document_type=target_document_type, field_mapping={"spans": "labeled_multi_spans", "relations": "binary_relations"}, ), trim_adus=TextSpanTrimmer(layer="labeled_multi_spans"), sort_symmetric_relation_arguments=RelationArgumentSorter( relation_layer="binary_relations", label_whitelist=["semantically_same"], ), ) def remove_duplicate_relations(document: Union[BratDocument, BratDocumentWithMergedSpans]) -> None: if len(document.relations) > len(set(document.relations)): added = set() i = 0 while i < len(document.relations): relation = document.relations[i] if relation in added: logger.warning(f"doc_id={document.id}: Removing duplicate relation: {relation}") document.relations.pop(i) else: added.add(relation) i += 1 class SciArgConfig(BratConfig): def __init__( self, name: str, resolve_parts_of_same: bool = False, **kwargs, ): super().__init__(name=name, merge_fragmented_spans=True, **kwargs) self.resolve_parts_of_same = resolve_parts_of_same class SciArg(BratBuilder): BASE_DATASET_PATH = "DFKI-SLT/brat" BASE_DATASET_REVISION = "844de61e8a00dc6a93fc29dc185f6e617131fbf1" # Overwrite the default config to merge the span fragments. # The span fragments in SciArg come just from the new line splits, so we can merge them. # Actual span fragments are annotated via "parts_of_same" relations. BUILDER_CONFIGS = [ SciArgConfig(name=BratBuilder.DEFAULT_CONFIG_NAME), SciArgConfig(name="resolve_parts_of_same", resolve_parts_of_same=True), ] DOCUMENT_TYPES = { BratBuilder.DEFAULT_CONFIG_NAME: BratDocumentWithMergedSpans, "resolve_parts_of_same": BratDocument, } # we need to add None to the list of dataset variants to support the default dataset variant BASE_BUILDER_KWARGS_DICT = { dataset_variant: {"url": URL, "split_paths": SPLIT_PATHS} for dataset_variant in ["default", "resolve_parts_of_same", None] } def _generate_document(self, example, **kwargs): document = super()._generate_document(example, **kwargs) if self.config.resolve_parts_of_same: # we need to convert the document to a different type to be able to merge the spans: # SpansViaRelationMerger expects the spans to be of type LabeledSpan, # but the document has spans of type BratSpan converted_doc = document.as_type( ConvertedBratDocumentWithMergedSpans, field_mapping={ "spans": "labeled_spans", "relations": "binary_relations", }, keep_remaining=True, ) merged_document = SpansViaRelationMerger( relation_layer="binary_relations", link_relation_label="parts_of_same", create_multi_spans=True, result_document_type=ConvertedBratDocument, result_field_mapping={ "labeled_spans": "labeled_multi_spans", "binary_relations": "binary_relations", "span_attributes": "span_attributes", "relation_attributes": "relation_attributes", "notes": "notes", }, )(converted_doc) # convert back to BratDocument document = merged_document.as_type( BratDocument, field_mapping={"labeled_multi_spans": "spans", "binary_relations": "relations"}, keep_remaining=True, ) else: # some documents have duplicate relations, remove them remove_duplicate_relations(document) return document @property def document_converters(self) -> DocumentConvertersType: regex_partitioner = RegexPartitioner( partition_layer_name="labeled_partitions", # find matching tags, allow newlines in between (s flag) and capture the tag name pattern="<([^>/]+)>(?s:.)*?", label_group_id=1, label_whitelist=["Title", "Abstract", "H1"], skip_initial_partition=True, strip_whitespace=True, ) if not self.config.resolve_parts_of_same: return { TextDocumentWithLabeledSpansAndBinaryRelations: Pipeline( **get_common_converter_pipeline_steps( TextDocumentWithLabeledSpansAndBinaryRelations ) ), TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions: Pipeline( **get_common_converter_pipeline_steps( TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions ), add_partitions=regex_partitioner, ), } else: return { # TextDocumentWithLabeledSpansAndBinaryRelations: Pipeline( # **get_common_converter_pipeline_steps_with_resolve_parts_of_same( # TextDocumentWithLabeledSpansAndBinaryRelations # ) # ), # TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions: Pipeline( # **get_common_converter_pipeline_steps_with_resolve_parts_of_same( # TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions # ), # add_partitions=regex_partitioner, # ), TextDocumentWithLabeledMultiSpansAndBinaryRelations: Pipeline( **get_common_converter_pipeline_steps_with_resolve_parts_of_same( TextDocumentWithLabeledMultiSpansAndBinaryRelations ) ), TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions: Pipeline( **get_common_converter_pipeline_steps_with_resolve_parts_of_same( TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions ), add_partitions=regex_partitioner, ), }