File size: 6,222 Bytes
2d9edd3 cc9f1ec caab546 cc9f1ec caab546 f4ff05c caab546 f4ff05c 982d568 caab546 cc9f1ec f4ff05c caab546 f4ff05c caab546 f4ff05c 982d568 caab546 982d568 caab546 982d568 f4ff05c caab546 f4ff05c caab546 |
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 |
from pie_modules.document.processing import (
RegexPartitioner,
RelationArgumentSorter,
SpansViaRelationMerger,
TextSpanTrimmer,
)
from pie_modules.documents import (
TextDocumentWithLabeledMultiSpansAndBinaryRelations,
TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
TextDocumentWithLabeledSpansAndBinaryRelations,
TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
)
from pytorch_ie.core import Document
from pie_datasets.builders import BratBuilder, BratConfig
from pie_datasets.builders.brat import BratDocument, BratDocumentWithMergedSpans
from pie_datasets.core.dataset import DocumentConvertersType
from pie_datasets.document.processing import Caster, Pipeline
URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
SPLIT_PATHS = {"train": "compiled_corpus"}
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"],
),
)
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"],
),
)
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:
document = SpansViaRelationMerger(
relation_layer="relations",
link_relation_label="parts_of_same",
create_multi_spans=True,
result_document_type=BratDocument,
result_field_mapping={"spans": "spans", "relations": "relations"},
)(document)
return document
@property
def document_converters(self) -> DocumentConvertersType:
regex_partitioner = RegexPartitioner(
partition_layer_name="labeled_partitions",
pattern="<([^>/]+)>.*</\\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,
),
}
|