Upload sciarg.py

sciarg.py

@@ -0,0 +1,349 @@
+import glob
+import logging
+from dataclasses import dataclass
+from os import listdir, path
+from typing import Dict, List, Optional
+
+import datasets
+from datasets import BuilderConfig, DatasetInfo, Features, Sequence, SplitGenerator, Value
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class BratConfig(BuilderConfig):
+    """BuilderConfig for BRAT."""
+
+    url: str = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"  # type: ignore
+    description: Optional[str] = '''This 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.'''
+    citation: Optional[str] = """|
+  @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}
+  }"""
+    homepage: Optional[str] = "https://github.com/anlausch/ArguminSci"
+
+    subdirectory_mapping: Optional[Dict[str, str]] = None
+    file_name_blacklist: Optional[List[str]] = None
+    ann_file_extension: str = "ann"
+    txt_file_extension: str = "txt"
+
+
+class Brat(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = BratConfig
+    BUILDER_CONFIG_CLASS.file_name_blacklist = ["A28"]
+    def _info(self):
+        return DatasetInfo(
+            description=self.config.description,
+            citation=self.config.citation,
+            homepage=self.config.homepage,
+            features=Features(
+                {
+                    "context": Value("string"),
+                    "file_name": Value("string"),
+                    "spans": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "locations": Sequence(
+                                {
+                                    "start": Value("int32"),
+                                    "end": Value("int32"),
+                                }
+                            ),
+                            "text": Value("string"),
+                        }
+                    ),
+                    "relations": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "arguments": Sequence(
+                                {"type": Value("string"), "target": Value("string")}
+                            ),
+                        }
+                    ),
+                    "equivalence_relations": Sequence(
+                        {
+                            "type": Value("string"),
+                            "targets": Sequence(Value("string")),
+                        }
+                    ),
+                    "events": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "trigger": Value("string"),
+                            "arguments": Sequence(
+                                {"type": Value("string"), "target": Value("string")}
+                            ),
+                        }
+                    ),
+                    "attributions": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "target": Value("string"),
+                            "value": Value("string"),
+                        }
+                    ),
+                    "normalizations": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "target": Value("string"),
+                            "resource_id": Value("string"),
+                            "entity_id": Value("string"),
+                        }
+                    ),
+                    "notes": Sequence(
+                        {
+                            "id": Value("string"),
+                            "type": Value("string"),
+                            "target": Value("string"),
+                            "note": Value("string"),
+                        }
+                    ),
+                }
+            ),
+        )
+
+    @staticmethod
+    def _get_location(location_string):
+        parts = location_string.split(" ")
+        assert (
+            len(parts) == 2
+        ), f"Wrong number of entries in location string. Expected 2, but found: {parts}"
+        return {"start": int(parts[0]), "end": int(parts[1])}
+
+    @staticmethod
+    def _get_span_annotation(annotation_line):
+        """
+        example input:
+        T1	Organization 0 4	Sony
+        """
+
+        _id, remaining, text = annotation_line.split("\t", maxsplit=2)
+        _type, locations = remaining.split(" ", maxsplit=1)
+        return {
+            "id": _id,
+            "text": text,
+            "type": _type,
+            "locations": [Brat._get_location(loc) for loc in locations.split(";")],
+        }
+
+    @staticmethod
+    def _get_event_annotation(annotation_line):
+        """
+        example input:
+        E1	MERGE-ORG:T2 Org1:T1 Org2:T3
+        """
+        _id, remaining = annotation_line.strip().split("\t")
+        args = [dict(zip(["type", "target"], a.split(":"))) for a in remaining.split(" ")]
+        return {
+            "id": _id,
+            "type": args[0]["type"],
+            "trigger": args[0]["target"],
+            "arguments": args[1:],
+        }
+
+    @staticmethod
+    def _get_relation_annotation(annotation_line):
+        """
+        example input:
+        R1	Origin Arg1:T3 Arg2:T4
+        """
+
+        _id, remaining = annotation_line.strip().split("\t")
+        _type, remaining = remaining.split(" ", maxsplit=1)
+        args = [dict(zip(["type", "target"], a.split(":"))) for a in remaining.split(" ")]
+        return {"id": _id, "type": _type, "arguments": args}
+
+    @staticmethod
+    def _get_equivalence_relation_annotation(annotation_line):
+        """
+        example input:
+        *	Equiv T1 T2 T3
+        """
+        _, remaining = annotation_line.strip().split("\t")
+        parts = remaining.split(" ")
+        return {"type": parts[0], "targets": parts[1:]}
+
+    @staticmethod
+    def _get_attribute_annotation(annotation_line):
+        """
+        example input (binary: implicit value is True, if present, False otherwise):
+        A1	Negation E1
+        example input (multi-value: explicit value)
+        A2	Confidence E2 L1
+        """
+
+        _id, remaining = annotation_line.strip().split("\t")
+        parts = remaining.split(" ")
+        # if no value is present, it is implicitly "true"
+        if len(parts) == 2:
+            parts.append("true")
+        return {
+            "id": _id,
+            "type": parts[0],
+            "target": parts[1],
+            "value": parts[2],
+        }
+
+    @staticmethod
+    def _get_normalization_annotation(annotation_line):
+        """
+        example input:
+        N1	Reference T1 Wikipedia:534366	Barack Obama
+        """
+        _id, remaining, text = annotation_line.split("\t", maxsplit=2)
+        _type, target, ref = remaining.split(" ")
+        res_id, ent_id = ref.split(":")
+        return {
+            "id": _id,
+            "type": _type,
+            "target": target,
+            "resource_id": res_id,
+            "entity_id": ent_id,
+        }
+
+    @staticmethod
+    def _get_note_annotation(annotation_line):
+        """
+        example input:
+        #1	AnnotatorNotes T1	this annotation is suspect
+        """
+        _id, remaining, note = annotation_line.split("\t", maxsplit=2)
+        _type, target = remaining.split(" ")
+        return {
+            "id": _id,
+            "type": _type,
+            "target": target,
+            "note": note,
+        }
+
+    @staticmethod
+    def _read_annotation_file(filename):
+        """
+        reads a BRAT v1.3 annotations file (see https://brat.nlplab.org/standoff.html)
+        """
+
+        res = {
+            "spans": [],
+            "events": [],
+            "relations": [],
+            "equivalence_relations": [],
+            "attributions": [],
+            "normalizations": [],
+            "notes": [],
+        }
+
+        with open(filename) as file:
+            for i, line in enumerate(file):
+                if len(line.strip()) == 0:
+                    continue
+                ann_type = line[0]
+
+                # strip away the new line character
+                if line.endswith("\n"):
+                    line = line[:-1]
+
+                if ann_type == "T":
+                    res["spans"].append(Brat._get_span_annotation(line))
+                elif ann_type == "E":
+                    res["events"].append(Brat._get_event_annotation(line))
+                elif ann_type == "R":
+                    res["relations"].append(Brat._get_relation_annotation(line))
+                elif ann_type == "*":
+                    res["equivalence_relations"].append(
+                        Brat._get_equivalence_relation_annotation(line)
+                    )
+                elif ann_type in ["A", "M"]:
+                    res["attributions"].append(Brat._get_attribute_annotation(line))
+                elif ann_type == "N":
+                    res["normalizations"].append(Brat._get_normalization_annotation(line))
+                elif ann_type == "#":
+                    res["notes"].append(Brat._get_note_annotation(line))
+                else:
+                    raise ValueError(
+                        f'unknown BRAT annotation id type: "{line}" (from file {filename} @line {i}). '
+                        f"Annotation ids have to start with T (spans), E (events), R (relations), "
+                        f"A (attributions), or N (normalizations). See "
+                        f"https://brat.nlplab.org/standoff.html for the BRAT annotation file "
+                        f"specification."
+                    )
+        return res
+
+    def _generate_examples(self, files=None, directory=None):
+        """Read context (.txt) and annotation (.ann) files."""
+        if files is None:
+            assert (
+                directory is not None
+            ), "If files is None, directory has to be provided, but it is also None."
+            _files = glob.glob(f"{directory}/*.{self.config.ann_file_extension}")
+            files = sorted(path.splitext(fn)[0] for fn in _files)
+
+        for filename in files:
+            basename = path.basename(filename)
+            if (
+                self.config.file_name_blacklist is not None
+                and basename in self.config.file_name_blacklist
+            ):
+                logger.info(f"skip annotation file: {basename} (blacklisted)")
+                continue
+
+            ann_fn = f"{filename}.{self.config.ann_file_extension}"
+            brat_annotations = Brat._read_annotation_file(ann_fn)
+
+            txt_fn = f"{filename}.{self.config.txt_file_extension}"
+            txt_content = open(txt_fn).read()
+            brat_annotations["context"] = txt_content
+            brat_annotations["file_name"] = basename
+
+            yield basename, brat_annotations
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        subdirectory_mapping = self.config.subdirectory_mapping
+
+        # since subclasses of BuilderConfig are not allowed to define
+        # attributes without defaults, check here
+        assert self.config.url is not None, "data url not specified"
+
+        # if url points to a local directory, just point to that
+        if path.exists(self.config.url) and path.isdir(self.config.url):
+            data_dir = self.config.url
+        # otherwise, download and extract
+        else:
+            data_dir = dl_manager.download_and_extract(self.config.url)
+        logging.info(f"load from data dir: {data_dir}")
+
+        # if no subdirectory mapping is provided, ...
+        if subdirectory_mapping is None:
+            # ... use available subdirectories as split names ...
+            subdirs = [f for f in listdir(data_dir) if path.isdir(path.join(data_dir, f))]
+            if len(subdirs) > 0:
+                subdirectory_mapping = {subdir: subdir for subdir in subdirs}
+            else:
+                # ... otherwise, default to a single train split with the base directory
+                subdirectory_mapping = {"": "train"}
+
+        return [
+            SplitGenerator(
+                name=split,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "directory": path.join(data_dir, subdir),
+                },
+            )
+            for subdir, split in subdirectory_mapping.items()
+        ]