sciarg / sciarg.py
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use pie-documents 0.1.0
ad58881 verified
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:.)*?</\\1>",
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,
),
}