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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- expert-generated
-license: []
-multilinguality:
-- monolingual
-pretty_name: SciArg
-size_categories:
-- 1K<n<10K
-source_datasets:
-- dr inventor corpus
-tags:
-- argument mining
-- scientific text
-- relation extraction
-- argumentative discourse unit recognition
-task_categories:
-- token-classification
-task_ids: []
----
-
-# Dataset Card for "sciarg"
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/anlausch/ArguminSci](https://github.com/anlausch/ArguminSci)
-- **Repository:** [https://github.com/anlausch/ArguminSci](https://github.com/anlausch/ArguminSci)
-- **Paper:** [An argument-annotated corpus of scientific publications](https://aclanthology.org/W18-5206.pdf)
-- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Dataset Summary
-
-The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing 
-fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific 
-publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of 
-scientific writing.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-The language in the dataset is English.
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-- `document_id`: the base file name, e.g. "A28"
-- `text`: the parsed text of the scientific publication in the XML format
-- `text_bound_annotations`: span annotations that mark argumentative discourse units (ADUs). Each entry has the following fields: `offsets`, `text`, `type`, and `id`.
-- `relations`: binary relation annotations that mark the argumentative relations that hold between a head and a tail ADU. Each entry has the following fields: `id`, `head`, `tail`, and `type` where `head` and `tail` each have the fields: `ref_id` and `role`. 
-
-### Data Splits
-
-The dataset consists of a single `train` split that has 40 documents.
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
-
-```
-@inproceedings{lauscher2018b,
-  title = {An argument-annotated corpus of scientific publications},
-  booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
-  publisher = {Association for Computational Linguistics},
-  author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
-  address = {Brussels, Belgium},
-  year = {2018},
-  pages = {40–46}
-}
-
-```
-
-### Contributions
-
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"full": {"description": "This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \nscientific writing.\n", "citation": "@inproceedings{lauscher2018b,\n  title = {An argument-annotated corpus of scientific publications},\n  booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\n  publisher = {Association for Computational Linguistics},\n  author = {Lauscher, Anne and Glava\u000b{s}, Goran and Ponzetto, Simone Paolo},\n  address = {Brussels, Belgium},\n  year = {2018},\n  pages = {40\u201346}\n}\n", "homepage": "https://github.com/anlausch/ArguminSci", "license": "", "features": {"document_id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "text_bound_annotations": [{"offsets": {"feature": [{"dtype": "int32", "id": null, "_type": "Value"}], "length": -1, "id": null, "_type": "Sequence"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}], "relations": [{"id": {"dtype": "string", "id": null, "_type": "Value"}, "head": {"ref_id": {"dtype": "string", "id": null, "_type": "Value"}, "role": {"dtype": "string", "id": null, "_type": "Value"}}, "tail": {"ref_id": {"dtype": "string", "id": null, "_type": "Value"}, "role": {"dtype": "string", "id": null, "_type": "Value"}}, "type": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "sciarg", "config_name": "full", "version": "1.0.0", "splits": {"train": {"name": "train", "num_bytes": 3759081, "num_examples": 40, "dataset_name": "sciarg"}}, "download_checksums": {"http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip": {"num_bytes": 1129621, "checksum": "380274895b21a89c0e617d13e9d879e2a1ade64f8cc9a7657902debfe4156665"}}, "download_size": 1129621, "post_processing_size": null, "dataset_size": 3759081, "size_in_bytes": 4888702}}

full/sciarg-train.parquet

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sciarg.py

@@ -1,368 +0,0 @@
-import glob
-from dataclasses import dataclass
-from typing import Dict, List
-from pathlib import Path
-
-import datasets
-
-
-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 None
-
-            info = fields[1].split()
-
-            ann["type"] = info[0]
-            ann["ref_id"] = info[1]
-            example["notes"].append(ann)
-
-    return example
-
-
-_CITATION = """\
-@inproceedings{lauscher2018b,
-  title = {An argument-annotated corpus of scientific publications},
-  booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
-  publisher = {Association for Computational Linguistics},
-  author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
-  address = {Brussels, Belgium},
-  year = {2018},
-  pages = {40–46}
-}
-"""
-_DESCRIPTION = """\
-The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing 
-fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific 
-publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of 
-scientific writing.
-"""
-_URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
-_HOMEPAGE = "https://github.com/anlausch/ArguminSci"
-
-
-@dataclass
-class SciArgConfig(datasets.BuilderConfig):
-    data_url = _URL
-    subdirectory_mapping = {"compiled_corpus": datasets.Split.TRAIN}
-    filename_blacklist = []  #["A28"]
-
-
-class SciArg(datasets.GeneratorBasedBuilder):
-    """Scientific Argument corpus"""
-
-    DEFAULT_CONFIG_CLASS = SciArgConfig
-
-    BUILDER_CONFIGS = [
-        SciArgConfig(
-            name="full",
-            version="1.0.0",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "full"
-
-    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features(
-            {
-                "document_id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "text_bound_annotations": [
-                    {
-                        "offsets": datasets.Sequence([datasets.Value("int32")]),
-                        "text": datasets.Value("string"),
-                        "type": datasets.Value("string"),
-                        "id": datasets.Value("string"),
-                    }
-                ],
-                "relations": [
-                    {
-                        "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"),
-                    }
-                ],
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
-        """Returns SplitGenerators."""
-        data_dir = self.config.data_dir or Path(dl_manager.download_and_extract(self.config.data_url))
-
-        return [
-            datasets.SplitGenerator(name=split, gen_kwargs={"filepath": data_dir / subdir})
-            for subdir, split in self.config.subdirectory_mapping.items()
-        ]
-
-    def _generate_examples(self, filepath):
-        for txt_file in glob.glob(filepath / "*.txt"):
-
-            brat_parsed = parse_brat_file(Path(txt_file))
-            if brat_parsed["document_id"] in self.config.filename_blacklist:
-                continue
-            relevant_subset = {f_name: brat_parsed[f_name] for f_name in self.info.features}
-            yield brat_parsed["document_id"], relevant_subset