initial commit

CHANGELOG

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+The purpose of this file is to state changes that where made to the dataset reader
+and further files taken from https://github.com/boschresearch/mulms-az-codi2023.
+
+Changes:
+
+- The file mulms.py originates from source/data_handling/mulms_dataset.py
+- The constants SENT_TYPE, MATSCI_SENT_TYPE, TOKEN_TYPE, SKIPPING_TYPE,
+  ENTITY_TYPE, RELATION_TYPE, PASSAGE_TYPE and DOCUMENT_METADATA_TYPE were
+  added to mulms.py from source/constants/constants.py
+- The functions get_token_indices_for_annot(), get_token_index_for_annot_if_subtoken()
+  were added to mulms.py from source/data_handling/util.py
+- The variables az_content_labels, az_structure_labels, meas_labels, mulms_ne_labels, rel_labels,
+  ne_labels_set, all_az_labels, meas_labels_set and rel_label_set were added to mulms.py
+  from source/constants/constants.py

COPYRIGHT

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+Experiment resources related to the MuLMS corpus.
+Copyright (c) 2023 Robert Bosch GmbH
+
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+along with this program. If not, see <https://www.gnu.org/licenses/>.

LICENSE_CODE

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MuLMS.py

@@ -0,0 +1,753 @@
+# Experiment resources related to the MuLMS corpus.
+# Copyright (c) 2023 Robert Bosch GmbH
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+# 24 October 2023 - Modified by Timo Schrader (find all changes in CHANGELOG)
+
+"""
+This module contains the HuggingFace dataset reader for the "Multi-Layer Materials Science Corpus (MuLMS)"
+"""
+
+from dataclasses import dataclass
+from os import listdir
+from os.path import exists, join
+
+import datasets
+import pandas as pd
+from datasets.utils.download_manager import DownloadManager
+from puima.collection_utils import DocumentCollection
+
+SENT_TYPE = "de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence"
+MATSCI_SENT_TYPE = "webanno.custom.MatSci_Sentence"
+TOKEN_TYPE = "de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Token"
+SKIPPING_TYPE = "webanno.custom.MatSci_Skipping"
+ENTITY_TYPE = "webanno.custom.MatSci_Entity"
+RELATION_TYPE = "webanno.custom.MatSci_Relations"
+PASSAGE_TYPE = "webanno.custom.MatSci_Passage"
+DOCUMENT_METADATA_TYPE = "de.tudarmstadt.ukp.dkpro.core.api.metadata.type.DocumentMetaData"
+
+
+def get_token_indices_for_annot(annot, sent_tokens, doc):
+    """
+    Retrieves indices of tokens within the list of sententence tokens (sent_tokens) for
+    the annotation annot.
+    param: annot - puima Annotation object
+    param: sent_tokens - list of puima Annotation objects (tokens of the sentence)
+    """
+    indices = [None, None]
+    for annot_token in doc.select_covered(TOKEN_TYPE, annot):  # noqa: F405
+        token_index = sent_tokens.index(annot_token)
+        if indices[0] is None or indices[0] > token_index:
+            indices[0] = token_index
+        if indices[1] is None or indices[1] < token_index:
+            indices[1] = token_index
+    return tuple(indices)
+
+
+def get_token_index_for_annot_if_subtoken(annot, sent_tokens, doc):
+    """
+    Retrieves indices of tokens within the list of sententence tokens (sent_tokens) for
+    the annotation annot.
+    param: annot - puima Annotation object
+    param: sent_tokens - list of puima Annotation objects (tokens of the sentence)
+    """
+    annot_token = next(doc.select_covering(TOKEN_TYPE, annot))  # noqa: F405
+    token_index = sent_tokens.index(annot_token)
+    return (token_index, token_index)
+
+
+az_content_labels: list = [
+    "Experiment",
+    "Results",
+    "Exp_Preparation",
+    "Exp_Characterization",
+    "Background_PriorWork",
+    "Explanation",
+    "Conclusion",
+    "Motivation",
+    "Background",
+]
+az_structure_labels: list = ["Metadata", "Caption", "Heading", "Abstract"]
+
+meas_labels: list = ["MEASUREMENT", "QUAL_MEASUREMENT", "O"]
+
+mulms_ne_labels: list = [
+    "MAT",
+    "NUM",
+    "VALUE",
+    "UNIT",
+    "PROPERTY",
+    "CITE",
+    "TECHNIQUE",
+    "RANGE",
+    "INSTRUMENT",
+    "SAMPLE",
+    "FORM",
+    "DEV",
+    "MEASUREMENT",
+]
+
+rel_labels: list = [
+    "hasForm",
+    "measuresProperty",
+    "usedAs",
+    "conditionProperty",
+    "conditionSampleFeatures",
+    "usesTechnique",
+    "conditionEnvironment",
+    "propertyValue",
+    "usedIn",
+    "conditionInstrument",
+    "dopedBy",
+    "takenFrom",
+    "usedTogether",
+]
+
+ne_labels_set = set(mulms_ne_labels)
+all_az_labels = set(az_content_labels) | set(az_structure_labels)
+meas_labels_set = set(meas_labels)
+rel_label_set: set = set(rel_labels)
+
+_CITATION = """\
+@InProceedings{schrader-etal-2023-mulms,
+title = {MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain},
+author={Timo Pierre Schrader, Teresa Bürkle, Sophie Henning, Sherry Tan, Matteo Finco, Stefan Grünewald, Maira Indrikova, Felix Hildebrand, Annemarie Friedrich
+},
+year={2023}
+},
+@InProceedings{schrader-etal-2023-mulms,
+title = {MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain},
+author={Timo Pierre Schrader, Matteo Finco, Stefan Grünewald, Felix Hildebrand, Annemarie Friedrich
+},
+year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset represents the Multi-Layer Material Science (MuLMS) corpus.
+It consists of 50 thoroughly annotated documents from the materials science domain and
+provides annotations for named entities, argumentative zoning (AZ), relation extraction,
+measurement classification and citation context retrieval. Please refer to our papers for
+more details about the MuLMS corpus.
+"""
+
+_HOMEPAGE = "https://github.com/boschresearch/mulms-az-codi2023"
+
+_LICENSE = "AGPL-3"
+
+_URLS = "data.zip"  # "Path to MuLMS-AZ files"
+
+
+@dataclass
+class MuLMSDatasetBuilderConfig(datasets.BuilderConfig):
+    """
+    Config class for the dataset class.
+    """
+
+    replace_heading_AZ_labels: bool = True
+    remove_figure_and_table_labels: bool = True
+
+
+class MuLMSDataset(datasets.GeneratorBasedBuilder):
+    """This dataset represents the Multi-Layer Material Science Corpus with 50 documents across multiple domains."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIG_CLASS = MuLMSDatasetBuilderConfig
+
+    BUILDER_CONFIGS = [
+        MuLMSDatasetBuilderConfig(
+            name="MuLMS_Corpus",
+            version=VERSION,
+            description="This part of the dataset covers all annotations.",
+        ),
+        datasets.BuilderConfig(
+            name="NER_Dependencies",
+            version=VERSION,
+            description="This part of the dataset represents Named Entities as dependencies (returned in CONLL format).",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "MuLMS_Corpus"
+
+    AZ_HEADING_REPLACEMENT_LABELS = [
+        "Supporting Information",
+        "Author Contribution",
+        "Confict of Interest",
+        "Acknowledgment",
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        """
+        Provides information about this dataset.
+
+        Returns:
+            datasets.DatasetInfo
+        """
+        if self.config.name == "default":
+            self.config.name = self.DEFAULT_CONFIG_NAME
+        if self.config.name == "MuLMS_Corpus":
+            features: datasets.Features = datasets.Features(
+                {
+                    "doc_id": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "beginOffset": datasets.Value("int32"),
+                    "endOffset": datasets.Value("int32"),
+                    "AZ_labels": datasets.Value("string"),
+                    "Measurement_label": datasets.Value("string"),
+                    "NER_labels": datasets.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "id": datasets.Value("int32"),
+                            "value": datasets.Value("string"),
+                            "begin": datasets.Value("string"),
+                            "end": datasets.Value("string"),
+                            "tokenIndices": datasets.Sequence(datasets.Value("int32")),
+                        }
+                    ),
+                    "NER_labels_BILOU": datasets.Sequence(datasets.Value("string")),
+                    "relations": datasets.Sequence(
+                        {
+                            "ne_id_gov": datasets.Value("int32"),
+                            "ne_id_dep": datasets.Value("int32"),
+                            "label": datasets.Value("string"),
+                        }
+                    ),
+                    "docFileName": datasets.Value("string"),
+                    "data_split": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "NER_Dependencies":
+            features: datasets.Features = datasets.Features(
+                {
+                    "ID": datasets.Value("int32"),
+                    "sentence": datasets.Value("string"),
+                    "token_id": datasets.Value("int32"),
+                    "token_text": datasets.Value("string"),
+                    "NE_Dependencies": datasets.Value("string"),
+                    "data_split": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> list:
+        """
+        Downloads files from URL or reads them from the file system and provides _generate_examples
+        with necessary information.
+
+        Args:
+            dl_manager (DownloadManager): Handles data retrieval
+
+        Returns:
+            list: Information about files and splits
+        """
+
+        data_files: list = dl_manager.download_and_extract(_URLS)
+        self.data_dir: str = join(data_files, "data/mulms_corpus")
+        data_files = listdir(join(self.data_dir, "xmi"))
+        assert exists(
+            join(self.data_dir, "MuLMS_Corpus_Metadata.csv")
+        ), "MuLMS_Corpus_Metadata.csv is missing."
+
+        if "/" in data_files[0]:
+            data_files = [f.split("/")[-1] for f in data_files]
+        if "\\" in data_files[0]:
+            data_files = [f.split("\\")[-1] for f in data_files]
+
+        metadata_df: pd.DataFrame = pd.read_csv(
+            join(self.data_dir, "MuLMS_Corpus_Metadata.csv")
+        )
+        train_files: list = sorted(
+            [
+                f
+                for f in data_files
+                if any(
+                    name in f
+                    for name in list(metadata_df[metadata_df["set"].str.contains("train")]["name"])
+                )
+            ]
+        )
+        dev_files: list = sorted(
+            [
+                f
+                for f in data_files
+                if any(
+                    name in f for name in list(metadata_df[metadata_df["set"] == "dev"]["name"])
+                )
+            ]
+        )
+        test_files: list = sorted(
+            [
+                f
+                for f in data_files
+                if any(
+                    name in f for name in list(metadata_df[metadata_df["set"] == "test"]["name"])
+                )
+            ]
+        )
+
+        if self.config.name == "MuLMS_Corpus":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "xmi"),
+                        "files": train_files,
+                        "data_split": metadata_df[metadata_df["set"].str.contains("train")][
+                            ["name", "set", "category"]
+                        ],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "xmi"),
+                        "files": dev_files,
+                        "data_split": metadata_df[metadata_df["set"] == "dev"][
+                            ["name", "set", "category"]
+                        ],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "xmi"),
+                        "files": test_files,
+                        "data_split": metadata_df[metadata_df["set"] == "test"][
+                            ["name", "set", "category"]
+                        ],
+                    },
+                ),
+            ]
+
+        elif self.config.name == "NER_Dependencies":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "ne_dependencies_conll"),
+                        "files": [
+                            "ne_deps_train1.conllu",
+                            "ne_deps_train2.conllu",
+                            "ne_deps_train3.conllu",
+                            "ne_deps_train4.conllu",
+                            "ne_deps_train5.conllu",
+                        ],
+                        "data_split": None,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "ne_dependencies_conll"),
+                        "files": ["ne_deps_dev.conllu"],
+                        "data_split": None,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "dir": join(self.data_dir, "ne_dependencies_conll"),
+                        "files": ["ne_deps_test.conllu"],
+                        "data_split": None,
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, dir: str, files: list, data_split: pd.DataFrame):
+        """
+        Yields the data during runtime.
+
+        Args:
+            dir (str): Path to downloaded or local files.
+            files (list): List of filenames corresponding to the current split; must be contained within "dir"
+            data_split (pd.DataFrame): Category and train/dev/test split info for each document
+
+        Yields:
+            tuple: Yields document ID and dictionary with current data sample
+        """
+
+        if self.config.name == "MuLMS_Corpus":
+
+            doc_coll: DocumentCollection = DocumentCollection(
+                xmi_dir=dir, file_list=files)
+
+            split_info: str = None
+            category_info: str = None
+
+            for doc_name in doc_coll.docs:
+                doc = doc_coll.docs[doc_name]
+                for sent_id, sent_annot in enumerate(
+                    doc.select_annotations(SENT_TYPE)  # noqa: F405
+                ):
+                    sent_text = doc.get_covered_text(sent_annot)
+
+                    # Argumentative Zoning labels
+                    az_labels = set()
+                    for matsci_sent in doc.select_covered(
+                        MATSCI_SENT_TYPE, sent_annot  # noqa: F405
+                    ):
+                        content_info = matsci_sent.get_feature_value(
+                            "ContentInformation")
+                        struct_info = matsci_sent.get_feature_value(
+                            "StructureInformation")
+                        az_labels.add(content_info)
+                        az_labels.add(struct_info)
+                    if "None" in az_labels:
+                        az_labels.remove("None")
+                    if None in az_labels:
+                        az_labels.remove(None)
+                    # Extract Measurement related label
+                    sent_meas_label = list(
+                        set.intersection(az_labels, meas_labels))
+                    if len(sent_meas_label) == 2:
+                        sent_meas_label = "MEASUREMENT"
+                    elif len(sent_meas_label) == 1:
+                        sent_meas_label = sent_meas_label[0]
+                    else:
+                        sent_meas_label = "O"
+                    # Remove AZ labels that are not in structure / content tags defined
+                    az_labels = list(
+                        set.intersection(all_az_labels, az_labels)
+                    )  # keep only valid labels
+
+                    if len(az_labels) == 0:
+                        continue
+
+                    # Tokens
+                    sent_tokens = list(doc.select_covered(TOKEN_TYPE, sent_annot))  # noqa: F405
+                    sent_token_list = [0] * len(sent_tokens)
+
+                    token2idx = {}
+                    for i, token in enumerate(sent_tokens):
+                        token2idx[token.begin] = i
+                        sent_token_list[i] = doc.get_covered_text(
+                            token)  # token text
+
+                    # Named Entity annotations
+                    sent_offset: int = sent_annot.begin
+                    ner_labels: list = []
+                    ner_labels_duplicate_lookup: dict = (
+                        dict()
+                    )  # Used to detect duplicate Named Entity annotations
+                    ne_annot2id: dict = {}
+                    for ent_annot in doc.select_covered(ENTITY_TYPE, sent_annot):  # noqa: F405
+                        if ent_annot.get_feature_value("implicitEntity") is None:
+                            label = ent_annot.get_feature_value("value")
+                            if label in ne_labels_set:  # filter by applicable labels
+                                # retrieve token indices
+                                ent_indices = get_token_indices_for_annot(
+                                    ent_annot, sent_tokens, doc
+                                )
+                                if (
+                                    None in ent_indices
+                                ):  # happens if entity annotation is subtoken : choose covering token
+                                    try:
+                                        ent_indices = get_token_index_for_annot_if_subtoken(
+                                            ent_annot, sent_tokens, doc
+                                        )
+                                    except StopIteration:
+                                        pass
+                                    except ValueError:
+                                        pass
+                                if None in ent_indices:
+                                    continue
+
+                                try:
+                                    ne_annot2id[ent_annot] = ent_annot.id
+                                    ne_dict: dict = {
+                                        "text": doc.get_covered_text(ent_annot),
+                                        "id": ent_annot.id,
+                                        "value": label,
+                                        "begin": ent_annot.begin - sent_offset,
+                                        "end": ent_annot.end - sent_offset,
+                                        "tokenIndices": ent_indices,
+                                    }  # index of first + last token of the NE
+                                    if (
+                                        not tuple(
+                                            [ne_dict["value"],
+                                                ne_dict["begin"], ne_dict["end"]]
+                                        )
+                                        in ner_labels_duplicate_lookup.keys()
+                                    ):
+                                        ner_labels.append(ne_dict)
+                                        ner_labels_duplicate_lookup[
+                                            (
+                                                tuple(
+                                                    [
+                                                        ne_dict["value"],
+                                                        ne_dict["begin"],
+                                                        ne_dict["end"],
+                                                    ]
+                                                )
+                                            )
+                                        ] = ent_annot.id
+                                    else:
+                                        ne_annot2id[ent_annot] = ner_labels_duplicate_lookup[
+                                            (
+                                                tuple(
+                                                    [
+                                                        ne_dict["value"],
+                                                        ne_dict["begin"],
+                                                        ne_dict["end"],
+                                                    ]
+                                                )
+                                            )
+                                        ]
+                                except KeyError:
+                                    pass
+
+                    # Creating Nested Named Entity BIO Labels
+                    B: str = "B-{0}"
+                    I: str = "I-{0}"
+                    L: str = "L-{0}"
+                    O: str = "O"
+                    U: str = "U-{0}"
+                    ner_labels.sort(
+                        key=lambda x: (x["tokenIndices"]
+                                       [0], -x["tokenIndices"][1])
+                    )  # Work from left to right and prioritize longer strings
+                    nested_bilou_labels: list = [O] * len(sent_tokens)
+                    if len(ner_labels) > 0:
+                        for i in range(len(ner_labels)):
+                            begin_idx: int = ner_labels[i]["tokenIndices"][0]
+                            end_idx: int = ner_labels[i]["tokenIndices"][1]
+
+                            # Check whether there are already two NE annotation layers within this span
+                            skip_current_entity: bool = False
+                            for j in range(begin_idx, end_idx + 1):
+                                if (
+                                    nested_bilou_labels[j].count("+") == 2
+                                ):  # Already 3 annotations connected via "+"
+                                    skip_current_entity = True
+                                    break
+
+                            if skip_current_entity:
+                                continue
+
+                            tag: str = ner_labels[i]["value"]
+
+                            # Case of Unit Length Tag
+                            if begin_idx == end_idx:
+                                if nested_bilou_labels[begin_idx] == O:
+                                    nested_bilou_labels[begin_idx] = U.format(
+                                        tag)
+                                else:
+                                    nested_bilou_labels[begin_idx] += "+" + \
+                                        U.format(tag)
+                                continue
+
+                            # Tags that span over more than one token
+                            if nested_bilou_labels[begin_idx] == O:
+                                nested_bilou_labels[begin_idx] = B.format(tag)
+                            else:
+                                nested_bilou_labels[begin_idx] += "+" + \
+                                    B.format(tag)
+
+                            # Append all inside tags
+                            for j in range(begin_idx + 1, end_idx + 1):
+                                if j < end_idx:
+                                    if nested_bilou_labels[j] == O:
+                                        nested_bilou_labels[j] = I.format(tag)
+                                    else:
+                                        nested_bilou_labels[j] += "+" + \
+                                            I.format(tag)
+                                else:
+                                    if nested_bilou_labels[j] == O:
+                                        nested_bilou_labels[j] = L.format(tag)
+                                    else:
+                                        nested_bilou_labels[j] += "+" + \
+                                            L.format(tag)
+
+                    # positive relation instances
+                    rel_labels: list = []
+                    for rel_annot in doc.select_covered(RELATION_TYPE, sent_annot):  # noqa: F405
+                        label: str = rel_annot.get_feature_value(
+                            "RelationType")
+
+                        gov_annot = rel_annot.get_feature_value(
+                            "Governor", True)
+                        dep_annot = rel_annot.get_feature_value(
+                            "Dependent", True)
+
+                        gov_label = gov_annot.get_feature_value("value")
+
+                        if (
+                            label in rel_label_set
+                        ):  # only consider annotation if in selected set of relations
+
+                            # --- Adding transitive links --- #
+                            # conditionProperty + propertyValue --> conditionPropertyValue
+                            # measuresProperty  + propertyValue --> measuresPropertyValue
+                            # Note that this will also happen when the intermediate entity is implicit!
+                            if gov_label == "MEASUREMENT" and label in {
+                                "conditionProperty",
+                                "measuresProperty",
+                            }:
+                                for rel_annot2 in doc.select_covered(
+                                    RELATION_TYPE, sent_annot  # noqa: F405
+                                ):
+                                    label2: str = rel_annot2.get_feature_value(
+                                        "RelationType")
+                                    gov_annot2 = rel_annot2.get_feature_value(
+                                        "Governor", True)
+                                    dep_annot2 = rel_annot2.get_feature_value(
+                                        "Dependent", True)
+
+                                    if label2 == "propertyValue" and gov_annot2 == dep_annot:
+                                        if label == "conditionProperty":
+                                            transitiveLabel = "conditionPropertyValue"
+                                        elif label == "measuresProperty":
+                                            transitiveLabel = "measuresPropertyValue"
+                                        else:
+                                            assert False
+
+                                        try:
+                                            rel_labels.append(
+                                                {
+                                                    "ne_id_gov": ne_annot2id[gov_annot],
+                                                    "ne_id_dep": ne_annot2id[dep_annot2],
+                                                    "label": transitiveLabel,
+                                                }
+                                            )
+                                        except KeyError:
+                                            continue
+                            # --- End of adding transitive links --- #
+
+                            if (
+                                gov_annot.get_feature_value(
+                                    "value") not in ne_labels_set
+                                or dep_annot.get_feature_value("value") not in ne_labels_set
+                            ):
+                                # only considering relations between "valid" NE types for now
+                                continue
+                            if gov_annot is dep_annot:
+                                continue
+                            if (
+                                gov_annot.begin == dep_annot.begin
+                                and gov_annot.end == dep_annot.end
+                            ):
+                                # same span, continue
+                                continue
+
+                            if gov_annot not in ne_annot2id:
+                                # check if it's in a different sentence
+                                if doc.get_covered_text(dep_annot) == "nanoparticle-type":
+                                    continue
+                                sent_list2 = list(
+                                    doc.select_covering(SENT_TYPE, gov_annot)  # noqa: F405
+                                )
+                                try:
+                                    sent_list2.remove(sent_annot)
+                                except ValueError:
+                                    pass
+                                if len(sent_list2) == 0:
+                                    continue
+                                sent_annot2 = sent_list2[0]
+                                if sent_annot2 is not sent_annot:
+                                    # gov in different sentence, skipping cross-sentence links
+                                    continue
+                            if dep_annot not in ne_annot2id:
+                                sent_list2 = list(
+                                    doc.select_covering(SENT_TYPE, dep_annot)  # noqa: F405
+                                )
+                                try:
+                                    sent_list2.remove(sent_annot)
+                                except ValueError:
+                                    pass
+                                if len(sent_list2) == 0:
+                                    continue
+                                sent_annot2 = sent_list2[0]
+                                if sent_annot2 != sent_annot:
+                                    # dep in different sentence, skipping cross-sentence links
+                                    continue
+
+                            if gov_annot not in ne_annot2id:
+                                if gov_annot.get_feature_value("valueType") == "implicit":
+                                    # skip this case, implicit PROPERTY
+                                    continue
+                                assert False
+                            if dep_annot not in ne_annot2id:
+                                assert False
+                            rel_labels.append(
+                                {
+                                    "ne_id_gov": ne_annot2id[gov_annot],
+                                    "ne_id_dep": ne_annot2id[dep_annot],
+                                    "label": label,
+                                }
+                            )
+
+                    if split_info is None:
+                        split_info = data_split[data_split["name"]
+                                                == doc_name]["set"].values[0]
+                        category_info = data_split[data_split["name"] == doc_name][
+                            "category"
+                        ].values[0]
+
+                    # Iterator yields data sample sentence-wise
+                    yield doc_name + "/" + str(sent_id), {
+                        "doc_id": doc_name,
+                        "sentence": sent_text,
+                        "tokens": sent_token_list,
+                        "beginOffset": sent_annot.begin,
+                        "endOffset": sent_annot.end,
+                        "AZ_labels": az_labels,
+                        "Measurement_label": sent_meas_label,
+                        "NER_labels": ner_labels,
+                        "NER_labels_BILOU": nested_bilou_labels,
+                        # [(ne_index_in_list, ne_index_in_list, relation_name)]
+                        "relations": rel_labels,
+                        "docFileName": doc_name,
+                        "data_split": split_info,
+                        "category": category_info,
+                    }
+
+                split_info = None
+                category_info = None
+        elif self.config.name == "NER_Dependencies":
+            id: int = 0
+            sent_id: int = 0
+            sent_text: str = None
+            for i, f in enumerate(files):
+                split_info: str = (
+                    f"train{i+1}" if "train" in f else (
+                        "dev" if "dev" in f else "test")
+                )
+                with open(join(dir, f), mode="r", encoding="utf-8") as cf:
+                    conll_lines: list[str] = cf.read().splitlines()
+                for line in conll_lines:
+                    if line.startswith("#"):
+                        sent_text = line.split("# text = ")[-1]
+                        sent_id += 1
+                        continue
+                    elif line == "":
+                        continue
+                    t_id, t_text, deps = line.split("\t")
+                    yield id, {
+                        "ID": sent_id,
+                        "sentence": sent_text,
+                        "token_id": t_id,
+                        "token_text": t_text,
+                        "NE_Dependencies": deps,
+                        "data_split": split_info,
+                    }
+                    id += 1

MuLMS_Corpus_Metadata.csv

@@ -0,0 +1,51 @@
+Document Title,Authors,Journal,Publisher,DOI,name,Publication date,license,License link,set,category
+Electrochromic solar water splitting using a cathodic WO3 electrocatalyst,Ilknur Bayrak Pehlivan;Gamze Atak;Gunnar A. Niklasson;Lars Stolt;Marika Edoff;Tomas Edvinsson,Nano Energy,Elsevier,10.1016/j.nanoen.2020.105620,electrolysis_01,18.11.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train3,electrolysis
+Assembly of Pt Nanoparticles on Graphitized Carbon Nanofibers as Hierarchically Structured Electrodes,Nejc Hodnik;Luigi Romano;Primož Jovanovič;Francisco Ruiz-Zepeda;Marjan Bele;Filippo Fabbri;Luana Persano;Andrea Camposeo;Dario Pisignano,ACS Applied Nano Materials,American Chemical Society,10.1021/acsanm.0c01945,electrolysis_02,01.09.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,test,electrolysis
+Highly efficient oxygen evolution reaction via facile bubble transport realized by three-dimensionally stack-printed catalysts,Ye Ji Kim;Ahyoun Lim;Jong Min Kim;Donghoon Lim;Keun Hwa Chae;Eugene N. Cho;Hyeuk Jin Han;Ki Ung Jeon;Moohyun Kim;Gun Ho Lee;Gyu Rac Lee;Hyun S. Ahn;Hyun S. Park;Hyoungsoo Kim;Jin Young Kim;Yeon Sik Jung,Nature Communications,Nature Publishing Group,10.1038/s41467-020-18686-0,electrolysis_03,01.10.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train2,electrolysis
+Common-Ion Effect Triggered Highly Sustained Seawater Electrolysis with Additional NaCl Production,Pengsong Li;Shiyuan Wang;Imran Ahmed Samo;Xingheng Zhang;Zhaolei Wang;Cheng Wang;Yang Li;Yiyun Du;Yang Zhong;Congtian Cheng;Wenwen Xu;Xijun Liu;Yun Kuang;Zhiyi Lu;Xiaoming Sun,Research,AAAS,10.34133/2020/2872141,electrolysis_04,24.09.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,electrolysis
+Facilely synthesized nitrogen-doped reduced graphene oxide functionalized with copper ions as electrocatalyst for oxygen reduction,Nadia Garino;Juqin Zeng;Micaela Castellino;Adriano Sacco;Francesca Risplendi;Michele Re Fiorentin;Katarzyna Bejtka;Angelica Chiodoni;Damien Salomon;Jaime Segura-Ruiz;Candido F. Pirri;Giancarlo Cicero,npj 2D Materials and Applications,Nature Publishing Group,10.1038/s41699-020-00185-x,graphene_01,04.01.2021,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train1,graphene
+Direct Laser Writing of Transparent Polyimide Film for Supercapacitor,Fei Huang;Guoying Feng;Jiajia Yin;Sikun Zhou;Li Shen;Shutong Wang;Yun Luo,Nanomaterials,MDPI,10.3390/nano10122547,graphene_02,18.12.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,test,graphene
+Grain boundary excess volume and defect annealing of copper after high-pressure torsion,Bernd Oberdorfer;Daria Setman;Eva-Maria Steyskal;Anton Hohenwarter;Wolfgang Sprengel;Michael Zehetbauer;Reinhard Pippan;Roland Würschum,Acta Materialia,Elsevier,10.1016/j.actamat.2013.12.036,oastm_01,21.02.2014,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train5,steel
+Preparation and characterization of a dual-layer carbon film on 6H-SiC wafer using carbide-derived carbon process with subsequent chemical vapor deposition,Jian Sui;Xiufang Liu,Surface and Coatings Technology,Elsevier,10.1016/j.surfcoat.2013.08.005,oastm_02,13.08.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train4,semi
+Halide doping effects on transparent conducting oxides formed by aerosol assisted chemical vapour deposition,Nuruzzaman Noor;Ivan P. Parkin,Thin Solid Films,Elsevier,10.1016/j.tsf.2012.10.110,oastm_03,20.11.2012,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train1,semi
+The mechanical properties and toughening mechanisms of an epoxy polymer modified with polysiloxane-based core-shell particles,J. Chena;A. J. Kinloch;S. Sprenger;A. C. Taylor,Polymer,Elsevier,10.1016/j.polymer.2013.06.009,oastm_04,17.06.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train4,poly
+Fabrication of calcium phosphate through electrospinning and sintering of hydroxyapatite nanoparticles,Pierre-Alexis Mouthuy;Alison Crossley;Hua Ye,Materials Letters,Elsevier,10.1016/j.matlet.2013.04.110,oastm_05,10.05.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train2,poly
+Direct observation of solute-dislocation interaction on screw dislocation in a neutron irradiated modified 316 stainless steel using laser assisted 3DAP,M. Hatakeyama;S. Tamura;I. Yamagata,Materials Letters,Elsevier,10.1016/j.matlet.2014.01.109,oastm_06,25.01.2014,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train1,steel
+The effects of particle morphology on the analysis of discrete particle dispersion using Delaunay tessellation,D. J. Bray;S. G. Gilmour;F. J. Guild;A. C. Taylor,Composites Part A: Applied Science and Manufacturing,Elsevier,10.1016/j.compositesa.2013.07.003,oastm_07,12.07.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train5,poly
+The modelling of the toughening of epoxy polymers via silica nanoparticles The effects of volume fraction and particle size,D. J. Bray;P. Dittanet;F. J. Guild;A. J. Kinloch;K. Masania;R. A. Pearson;A. C. Taylor,Polymer,Elsevier,10.1016/j.polymer.2013.10.034,oastm_08,26.10.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train3,poly
+"High performance inverted bulk heterojunction solar cells by incorporation of dense, thin ZnO layers made using atmospheric atomic layer deposition",Robert L.Z. Hoye;David Muñoz-Rojas;Diana C. Iza;Kevin P.Musselman;Judith L. MacManus-Driscoll,Solar Energy Materials and Solar Cells,Elsevier,10.1016/j.solmat.2013.04.020,oastm_09,06.06.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,dev,steel
+Influences on the energy delivery of thin film photovoltaic modules,R. Gottschalg;T.R. Betts;A. Eeles;S.R. Williams;J. Zhu,Solar Energy Materials and Solar Cells,Elsevier,10.1016/j.solmat.2013.06.011,oastm_10,23.08.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train3,semi
+Minority carries lifetime in silicon photovoltaics: The effect of oxygen precipitation,J.D. Murphy;R.E. McGuire;K. Bothe;V.V. Voronkov;R.J. Falster,Solar Energy Materials and Solar Cells,Elsevier,10.1016/j.solmat.2013.06.018,oastm_11,15.07.2013,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train5,semi
+Preparation of High Performance and Ultra-Low Platinum Loading Membrane Electrode Assembly for PEMFC Commercial Application,Shengchu Liu;Shang Li;Ruyi Wang;Yan Rao;Qing Zhong;Kang Hong;Mu Pan,Journal of The Electrochemical Society,Electrochemical Society,10.1149/2.0151916jes,pemfc_02,12.12.2019,CC BY,https://creativecommons.org/licenses/by/4.0/legalcode,train1,pemfc
+Ink Solvent Dependence of the Ionomer Distribution in the Catalyst Layer of a PEMFC,Alin Orfanidi;Philipp J. Rheinländer;Nicole Schulte;Hubert A. Gasteiger,Journal of The Electrochemical Society,Electrochemical Society,10.1149/2.1251814jes,pemfc_03,15.11.2018,CC BY,https://creativecommons.org/licenses/by/4.0/legalcode,test,pemfc
+"Full Parametric Study of the Influence of Ionomer Content, Catalyst Loading and Catalyst Type on Oxygen and Ion Transport in PEM Fuel Cell Catalyst Layers",Robert Alink;Rajveer Singh;Patrick Schneider;Kläre Christmann;Johannes Schall;Roman Keding;Nada Zamel,Molecules,MDPI,10.3390/molecules25071523,pemfc_04,27.03.2020,CC BY,https://creativecommons.org/licenses/by/4.0/legalcode,train2,pemfc
+"Effects of carbon supports on Pt distribution, ionomer coverage and cathode performance for polymer electrolyte fuel cells",Young-Chul Park;Haruki Tokiwa;Katsuyoshi Kakinuma;Masahiro Watanabe;Makoto Uchida,Journal of Power Sources,Elsevier,10.1016/j.jpowsour.2016.02.091,pemfc_07,21.03.2016,CC BY,https://creativecommons.org/licenses/by/4.0/legalcode,train5,pemfc
+Applicability of double layer capacitance measurements to monitor local temperature changes at polymer electrolyte membrane fuel cell cathodes,Jarek P. Sabawa;Aliaksandr S. Bandarenka,Results in Chemistry,Elsevier,10.1016/j.rechem.2020.100078,pemfc_08,02.11.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train2,pemfc
+Electron and proton conductivity of Fe-N-C cathodes for PEM fuel cells: A model-based electrochemical impedance spectroscopy measurement,Tatyana Reshetenko;Alexey Serov;Madeleine Odgaard;Günter Randolf;Luigi Osmieri;Andrei Kulikovsky,Electrochemistry Communications,Elsevier,10.1016/j.elecom.2020.106795,pemfc_10,24.07.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train4,pemfc
+Performance hysteresis phenomena of anion exchange membrane fuel cells using an Fe–N–C cathode catalyst and an in-house-developed polymer electrolyte,Kanji Otsuji;Naoki Yokota;Donald A. Tryk;Katsuyoshi Kakinuma;Kenji Miyatake;Makoto Uchida,Journal of Power Sources,Elsevier,10.1016/j.jpowsour.2020.229407,pemfc_13,06.01.2021,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,pemfc
+Transport and Electrochemical Interface Properties of Ionomers in Low-Pt Loading Catalyst Layers: Effect of Ionomer Equivalent Weight and Relative Humidity,Sushmit Poojary;Muhammad Naoshad Islam;Udit N. Shrivastava;Edward P. L. Roberts;Kunal Karan,Molecules,MDPI,10.3390/molecules25153387,pemfc_14,26.07.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train3,pemfc
+Experimental Characterization and Simulation of Thermoplastic Polymer Flow Hesitation in Thin-Wall Injection Molding Using Direct In-Mold Visualization Technique,Francesco Regi;Patrick Guerrier;Yang Zhang;Guido Tosello,Micromachines,MDPI,10.3390/mi11040428,poly_01,19.04.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train2,poly
+Molecular Dynamics Simulation on the Interfacial Behavior of Over-Molded Hybrid Fiber Reinforced Thermoplastic Composites,Bingyan Jiang;Muhan Zhang;Liang Fu;Mingyong Zhou;Zhanyu Zhai,Polymers,MDPI,10.3390/polym12061270,poly_02,02.06.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train4,poly
+Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems,Ivan Kopal;Marta Harničárová;Jan Valíček;Jan Krmela;Ondrej Lukáč,Polymers,MDPI,10.3390/polym11061074,poly_04,21.06.2019,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train1,poly
+Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network,Ivan Kopal;Marta Harničárová;Jan Valíček;Milena Kušnerová,Polymers,MDPI,10.3390/polym9100519,poly_05,18.10.2017,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train3,poly
+Identification of the elasto-viscoplastic parameters for a thermoplastic polymer by instrumented indentation,A. Mokhtari;N. Tala Ighil,IOP Conference Series: Materials Science and Engineering,IOP Publishing,10.1088/1757-899X/461/1/012057,poly_07,12.2018,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train5,poly
+Determination of polymer melts flow-activation energy a function of wide range shear rate,G Toth;D Nagy;A Bata;K Belina,Journal of Physics: Conference Series,IOP Publishing,10.1088/1742-6596/1045/1/012040,poly_09,06.2018,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,test,poly
+Morphological Monte Carlo Simulation for Crystallization of Isotactic Polypropylene in a Temperature Gradient,Chunlei Ruan,Crystals,MDPI,10.3390/cryst9040213,poly_11,20.04.2019,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,poly
+"Application of UV photoluminescence imaging spectroscopy for stacking faults identification on thick, lightly n-type doped, 4°-off 4H-SiC epilayers",N. Thierry-Jebali;C. Kawahara;T. Miyazawa;H. Tsuchida;T. Kimoto,AIP Advances,American Institute of Physics,10.1063/1.4915128,semi_01,12.03.2015,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train5,semi
+Photoluminescence study of oxidation-induced faults in 4H-SiC epilayers,Yutaro Miyano;Ryosuke Asafuji;Shuhei Yagi;Yasuto Hijikata;Hiroyuki Yaguchi,AIP Advances,American Institute of Physics,10.1063/1.4938126,semi_02,14.12.2015,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train4,semi
+Behavior of SiC MOSFET under Short-Circuit during the On-State,Meiting Cui;Jinyuan Li;Yujie Du;Zhibin Zhao,IOP Conference Series: Materials Science and Engineering,IOP Publishing,10.1088/1757-899X/439/2/022026,semi_03,11.2018,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,train3,semi
+"Mobility improvement of 4H-SiC (0001) MOSFETs by a three-step process of H2 etching, SiO2 deposition, and interface nitridation",Keita Tachiki;Mitsuaki Kaneko;Tsunenobu Kimoto,Applied Physics Express,IOP Publishing,10.35848/1882-0786/abdcd9,semi_04,29.01.2021,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,semi
+Genesis and evolution of extended defects: The role of evolving interface instabilities in cubic SiC,Giuseppe Fisicaro;Corrado Bongiorno;Ioannis Deretzis;Filippo Giannazzo;Francesco La Via;Fabrizio Roccaforte;Marcin Zielinski;Massimo Zimbone;Antonino La Magna,Applied Physics Reviews,American Institute of Physics,10.1063/1.5132300,semi_05,28.04.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train2,semi
+Brominated Chemistry for Chemical Vapor Deposition of Electronic Grade SiC,Milan Yazdanfar;Örjan Danielsson;Emil Kalered;Pitsiri Sukkaew;Olle Kordina;Daniel Nilsson;Ivan G. Ivanov;Lars Ojamäe;Erik Janzén;Henrik Pedersen,Chemistry of Materials,American Chemical Society,10.1021/acs.chemmater.5b00074,semi_06,22.01.2015,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train1,semi
+Free-standing 2-inch bulk GaN crystal fabrication by HVPE using a carbon buffer layer,V V Voronenkov;A A Leonidov;N I Bochkareva;R I Gorbunov;P E Latyshev;Y S Lelikov;V S Kogotkov;A S Zubrilov;Y G Shreter,Journal of Physics: Conference Series,IOP Publishing,10.1088/1742-6596/1199/1/012004,semi_08,02.2019,CC-BY 3.0,https://creativecommons.org/licenses/by/3.0/legalcode,test,semi
+Rational Design of a Water-Storable Hierarchical Architecture Decorated with Amorphous Barium Oxide and Nickel Nanoparticles as a Solid Oxide Fuel Cell Anode with Excellent Sulfur Tolerance,Yufei Song;Wei Wang;Lei Ge;Xiaomin Xu;Zhenbao Zhang;Paulo Sérgio Barros Julião;Wei Zhou;Zongping Shao,Advanced Science,Wiley-VCH,10.1002/advs.201700337,sofc_01,15.09.2017,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,test,sofc
+Micro solid oxide fuel cell fabricated on porous stainless steel: a new strategy for enhanced thermal cycling ability,Kun Joong Kim;Byung Hyun Park;Sun Jae Kim;Younki Lee;Hongyeul Bae;Gyeong Man Choi,Scientific Reports,Nature Research,10.1038/srep22443,sofc_02,01.03.2016,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train3,sofc
+In Situ Impedance Analysis of Oxygen Exchange on Growing La0.6Sr0.4CoO3−δ Thin Films,Ghislain M. Rupp;Markus Kubicek;Alexander K. Opitz;Jürgen Fleig,ACS Applied Energy Materials,American Chemical Society,10.1021/acsaem.8b00586,sofc_03,20.08.2018,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train4,sofc
+3D Self‐Architectured Steam Electrode Enabled Efficient and Durable Hydrogen Production in a Proton‐Conducting Solid Oxide Electrolysis Cell at Temperatures Lower Than 600 °C,Wei Wu;Hanping Ding;Yunya Zhang;Yong Ding;Prashant Katiyar;Prasun K. Majumdar;Ting He;Dong Ding,Advanced Science,Wiley-VCH,10.1002/advs.201800360,sofc_04,31.08.2018,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,sofc
+The Influence of Magnetic Field on Fatigue and Mechanical Properties of a 35CrMo Steel,Qing Gu;Xiaxu Huang;Jiangtao Xi;Zhenfeng Gao,Metals,MDPI,10.3390/met11040542,steel_03,26.03.2021,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train5,steel
+Study of 13Cr-4Ni-(Mo) (F6NM) Steel Grade Heat Treatment for Maximum Hardness Control in Industrial Heats,Massimo De Sanctis;Gianfranco Lovicu;Massimiliano Buccioni;Angelo Donato;Maria Richetta;Alessandra Varone,Metals,MDPI,10.3390/met7090351,steel_04,06.09.2017,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,test,steel
+"Effect of Annealing Time and Temperature Parameters on the Microstructure, Hardness, and Strain-Hardening Coefficients of 42CrMo4 Steel",Mirosław Szala;Grzegorz Winiarski;Łukasz Wójcik;Tomasz Bulzak,Materials,MDPI,10.3390/ma13092022,steel_05,26.04.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train1,steel
+Study on Inclusions Distribution and Cyclic Fatigue Performance of Gear Steel 18CrNiMo7-6 Forging,Min Wang;Wei Xiao;Peng Gan;Chao Gu;Yan-Ping Bao,Metals,MDPI,10.3390/met10020201,steel_06,31.01.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train4,steel
+On the Influence of Control Type and Strain Rate on the Lifetime of 50CrMo4,Max Benedikt Geilen;Josef Arthur Schönherr;Marcus Klein;Dominik Sebastian Leininger;Alexander Giertler;Ulrich Krupp;Matthias Oechsner,Metals,MDPI,10.3390/met10111458,steel_07,30.10.2020,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,dev,steel
+Evolution of White Etching Bands in 100Cr6 Bearing Steel under Rolling Contact-Fatigue,Hanwei Fu;Pedro E. J. Rivera-Díaz-del-Castillo,Metals,MDPI,10.3390/met9050491,steel_08,27.04.2019,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train3,steel
+Effect of Pre-Corrosion Pits on Residual Fatigue Life for 42CrMo Steel,Dezheng Liu;Yan Li;Xiangdong Xie;Jing Zhao,Materials,MDPI,10.3390%2Fma12132130,steel_11,02.07.2019,CC-BY 4.0,https://creativecommons.org/licenses/by/4.0/legalcode,train2,steel

README.md

@@ -1,3 +1,286 @@
 ---
 license: cc-by-sa-4.0
+language:
+- en
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- fill-mask
+- token-classification
+- text-classification
+task_ids:
+- named-entity-recognition
+- slot-filling
+pretty_name: Multi-Layer Materials Science Corpus
 ---
+
+# Dataset Card for MuLMS
+
+<p>
+<img src="teaser.png">
+<em>Example annotation in the Multi-Layer Materials Science Corpus (image source: <a href="https://arxiv.org/abs/2310.15569"> MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain</a>)<em>
+</p>
+
+### Dataset Description
+
+The Multi-Layer Materials Science corpus (MuLMS) consists of 50 documents (licensed CC BY) from the materials science domain, spanning across the following 7 subareas: 
+"Electrolysis", "Graphene", "Polymer Electrolyte Fuel Cell (PEMFC)", "Solid Oxide Fuel Cell (SOFC)", "Polymers", "Semiconductors" and "Steel". 
+It was exhaustively annotated by domain experts. There are annotations on sentence-level and token-level for the following NLP tasks:
+
+* __Measurement Frames__: Measurement annotations are treated in a frame-like fashion, using the span type MEASUREMENT to mark the triggers (e.g.,
+was measured, is plotted) that introduce the Measurement frame to the discourse. Deciding whether a sentence contains a measurement trigger is treated as a
+sentence-level task, determining the span that triggers the measurement frame is treated as named entity recognition.
+* __Named Entities__: There are 12 token-level named entities (+ Measurement trigger) available in MuLMS. Named entities can span across multiple tokens.
+* __Relations__: MuLMS provides relations between pairs of entities. There are two types of relations: measurement-related relations and further relations.
+The first type always starts at Measurement trigger spans, the scond type does not start at a specific Measurement annotation.
+* __Argumentative Zones__: Each sentence in MuLMS is assigned a rhetorical function in the discourse (e.g., _Background_ or _Experiment_Preparation_). There are 12 argumentative
+  zones in MuLMS, which leads to a sentence-level classification task.
+
+You can find all experiment code files and further information in the [MuLMS-AZ Repo](https://github.com/boschresearch/mulms-az-codi2023) and [MuLMS Repo](https://github.com/boschresearch/mulms-wiesp2023).
+For dataset statistics, please refer to both papers listed below. There you can also find detailed explanation of all parts of MuLMS in very detail.
+
+- **Curated by:** [Bosch Center for AI](https://www.bosch-ai.com/) and [Bosch Research](https://www.bosch.com/research/)
+- **Funded by**: [Robert Bosch GmbH](https://www.bosch.de/)
+- **Language(s) (NLP):** English
+- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode.txt)
+
+## Dataset Details
+
+MuLMS provides all annotated files in UIMA CAS XMI format that can be used with annotation tools that can read these files such as [INCEpTION](https://inception-project.github.io/).
+
+__Important:__ To use the dataset reader, please install the UIMA CAS Python reader _puima_ using the following command: `pip install git+https://github.com/annefried/puima.git`.
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/boschresearch/mulms-az-codi2023, https://github.com/boschresearch/mulms-wiesp2023
+- **Paper:** https://aclanthology.org/2023.codi-1.1/, https://arxiv.org/abs/2310.15569
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+This dataset aims at information extraction from materials science documents. It enables the training of (neural) classifiers that can be used for downstream tasks such as NER and relation extraction.
+Please refer to both repos linked above for training BERT-like models on all NLP tasks provided in MuLMS.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+MuLMS offers two configs: _MuLMS_Corpus_, which loads the entire MuLMS dataset, and _NER_Dependecies_, which loads only Named Entities in CONLL format in order
+to train models in the _NER_as_dependency_parsing_ setting.
+
+MuLMS is divided into three split: _train_, _validation_, and _test_. Furthermore, _train_ is divided into five sub-splits, namely _tune1_,...,_tune5_.
+This allows for model training on four splits, early stopping on the fivth and remaining split, model picking on validation and evaluation only once on test.
+HuggingFace datasets do not support these sub-splits, hence they must be loaded as _train_ and post-processed and filtered afterward in a custom dataset loader.
+
+### Dataset Config _MuLMS_Corpus_
+
+- `doc_id`: ID of the source document that can be used to lookup the metadata of the paper in [MuLMS_Corpus_Metadata.csv](MuLMS_Corpus_Metadata.csv).
+- `sentence`: Each instance in the dataset corresponds to one sentence extracted from scientic papers. These sentences are listed in this field.
+- `tokens`: Pre-tokenized sentences. Each instance is a list of tokens.
+- `begin_offset`: Offset of the beginning of each sentence within the full text of the document.
+- `end_offset`: Offset of the end of each sentence within the full text of the document.
+- `AZ_labels`: The argumentative zone (= rhetorical function) of each sentence in the discourse of a materials science publication.
+- `Measurement_label`: Labels each sentence whether it contains a measurement description, i.e., measurement frame evoking trigger word, or not.
+- `NER_labels`: Contains lists with named entities (NEs) per instance. Every named entity uses one of n indices in these lists, i.e., every 0-th element belong to each other, ...
+  - `text`: List of tokens that are contained in the current sentence instance.
+  - `id`: Unique ID for each named entity
+  - `value`: The named entity class
+  - `begin`: Character offsets of the begin tokens of each NE
+  - `end`: Character offsets of the end tokens of each NE
+  - `tokenIndices`: Token index in the list of tokens
+- `NER_labels_BILOU`: BILOU tag sequence per token in the sentence (B = begin, I = inside, L = last, O = none, U = unit).
+- `relations`: Lists of relations between pair-wise entities. As with the named entities, each relation corresponds to the same index in all three lists (_ne_id_gov_, _ne_id_dep_, _label_)
+  - `ne_id_gov`: List of NE entity IDs that act as head of the relation
+  - `ne_id_dep`: List of NE entity IDs that are the tail of the relation
+  - `label`: Relation label between both entities
+- `docFileName`: Name of the source document in the corpus
+- `data_split`: Indicates the split which a document belongs to (tune1/2/3/4/5, dev, test)
+- `category`: One of 7 materials science sub-domains in MuLMS (SOFC, graphene, electrolysis, PEMFC, )
+
+### Dataset Config _NER_Dependencies_
+
+Each instance in this config refers to one token and carries a copy of the entire sentence, i.e., for _n_ tokens in a sentence, the text of the sentence is given _n_ times.
+
+- `index`: Unique instance ID for each token. 
+- `ID`: Sentence ID. As opposed to the other config, the sentences here are not sorted by document and provided in their full form for every token they belong to.
+- `Sentence`: Sentence string
+- `Token_ID`: Unique ID for each token within each sentence. ID is resetted for each new sentence.
+- `Token`: Token string
+- `NE_Dependencies`: The named entity tag of form _k:LABEL_ where _k_ refers to the ID of the begin token and _LABEL_ to the named entity. The entity ends at the token holding this
+- label.
+- `data_split`: Indicates the split which a document belongs to (tune1/2/3/4/5, dev, test)
+
+### Labels
+
+For the different layers, the following labels are available:
+
+* __Measurement Frames__:
+  * `Measurement`
+  * `Qual_Measurement`
+* __Named Entities__:
+  * `MAT`
+  * `NUM`
+  * `VALUE`
+  * `UNIT`
+  * `PROPERTY`
+  * `FORM`
+  * `MEASUREMENT` (measurement frame-evoking trigger)
+  * `CITE`
+  * `SAMPLE`
+  * `TECHNIQUE`
+  * `DEV`
+  * `RANGE`
+  * `INSTRUMENT`
+* __Relations__:
+  * `hasForm`
+  * `measuresProperty`
+  * `usedAs`
+  * `propertyValue`
+  * `conditionProperty`
+  * `conditionSample`
+  * `conditionPropertyValue`
+  * `usesTechnique`
+  * `measuresPropertyValue`
+  * `usedTogether`
+  * `conditionEnv`
+  * `usedIn`
+  * `conditionInstrument`
+  * `takenFrom`
+  * `dopedBy`
+* __Argumentative Zones__:
+  * `Motivation`
+  * `Background`
+    * `PriorWork`
+  * `Experiment`
+    * `Preparation`
+    * `Characterization`
+  * `Explanation`
+  * `Results`
+  * `Conclusion`
+  * `Heading`
+  * `Caption`
+  * `Metadata`
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+Keeping track of all relevant recent publications and experimental results for a research area is a challenging task. MuLMS addresses this problem by
+providing a large set of annotated documents that allow for training models that can be used for automated information extraction and answering search queries
+in materials science documents.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+You can find all the details for every document in this corpus in [MuLMS_Corpus_Metadata.csv](MuLMS_Corpus_Metadata.csv).
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+You can find all the authors for every document in this corpus in [MuLMS_Corpus_Metadata.csv](MuLMS_Corpus_Metadata.csv).
+
+#### Annotation process
+
+The annotation process included guideline design in dedicated discussion sessions. Afterward, the text files were annotated 
+using [INCEpTION](https://inception-project.github.io/).
+
+#### Who are the annotators?
+
+The annotators worked collaboratively to annotate the dataset in the best possible way. All people in this project either have background in materials science or computer
+science. This synergy enables to incorporate both views, the materials scientist view that has a deep knowledge about the topics themselves as well as the CS view that
+always looks at processing text data automatically in a structured fashion.
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+This dataset does not contain any personal, sensitive or private data. MuLMS builds upon publicly available scientific publications and all authors are credited accordingly.
+
+## Citation
+
+If you use our software or dataset in your scientific work, please cite both papers:
+
+**BibTeX:**
+```
+@misc{schrader2023mulms,
+      title={MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain}, 
+      author={Timo Pierre Schrader and Matteo Finco and Stefan Grünewald and Felix Hildebrand and Annemarie Friedrich},
+      year={2023},
+      eprint={2310.15569},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+@inproceedings{schrader-etal-2023-mulms,
+    title = "{M}u{LMS}-{AZ}: An Argumentative Zoning Dataset for the Materials Science Domain",
+    author = {Schrader, Timo  and
+      B{\"u}rkle, Teresa  and
+      Henning, Sophie  and
+      Tan, Sherry  and
+      Finco, Matteo  and
+      Gr{\"u}newald, Stefan  and
+      Indrikova, Maira  and
+      Hildebrand, Felix  and
+      Friedrich, Annemarie},
+    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.codi-1.1",
+    doi = "10.18653/v1/2023.codi-1.1",
+    pages = "1--15",
+}
+```
+
+## Changes
+
+Changes to the source code from the original repo are listed in the [CHANGELOG](CHANGELOG) file.
+
+## Copyright
+
+```
+Experiment resources related to the MuLMS corpus.
+Copyright (c) 2023 Robert Bosch GmbH
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published
+by the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU Affero General Public License for more details.
+You should have received a copy of the GNU Affero General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+```
+
+## License
+
+This software is open-sourced under the AGPL-3.0 license. See the
+[LICENSE_CODE](LICENSE_CODE) file for details.
+The MuLMS corpus is released under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode.txt) license. See the [LICENSE_CORPUS](LICENSE_CORPUS) file for details.
+
+## Dataset Card Authors
+
+* Timo Pierre Schrader (Bosch Center for AI, University of Augsburg)
+* Matteo Finco (Bosch Research)
+* Stefan Grünewald (Bosch Center for AI, University of Stuttgart)
+* Felix Hildebrand (Bosch Research)
+* Annemarie Friedrich (University of Augsburg)
+
+## Dataset Card Contact
+
+For all questions, please contact [Timo Schrader](mailto:timo.schrader@de.bosch.com).

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