sciarg.py

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