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men.py

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+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+from seacrowd.utils.schemas import kb_features
+
+_CITATION = """\
+@misc{chanthran2024malaysian,
+      title={Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction},
+      author={Mohan Raj Chanthran and Lay-Ki Soon and Huey Fang Ong and Bhawani Selvaretnam},
+      year={2024},
+      eprint={2402.14521},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "men"
+
+_DESCRIPTION = """\
+The Malaysian English News (MEN) dataset includes 200 Malaysian English news article with human annotated entities and relations (in total 6,061 entities and 3,268 relation instances).
+Malaysian English combines elements of standard English with Malay, Chinese, and Indian languages. Four human annotators were split into 2 groups, each group annotated 100 news articles
+and inter-annotator agreement was calculated between 2 or more annotators working on the same task (entity annotation; F1-score 0.82, relation annotation; F1-score 0.51).
+"""
+
+_HOMEPAGE = "https://github.com/mohanraj-nlp/MEN-Dataset/tree/main"
+
+_LANGUAGES = ["eng"]
+
+_LICENSE = Licenses.MIT.value
+
+_LOCAL = False
+
+_URLS = "https://github.com/mohanraj-nlp/MEN-Dataset/archive/refs/heads/main.zip"
+
+_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MENDataset(datasets.GeneratorBasedBuilder):
+    """The Malaysian English News dataset comprises 200 articles with 6,061 annotated entities and 3,268 relations.
+    Inter-annotator agreement for entity annotation was high (F1-score 0.82), but lower for relation annotation (F1-score 0.51)."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_kb",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_kb",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "article": datasets.Value("string"),
+                    "entities": datasets.Sequence({"id": datasets.Value("int64"), "label": datasets.Value("string"), "position": {"start": datasets.Value("int32"), "end": datasets.Value("int32")}}),
+                    "relations": datasets.Sequence({"id": datasets.Value("string"), "head": datasets.Value("int32"), "tail": datasets.Value("int32"), "relation": datasets.Value("string"), "relation_source": datasets.Value("string")}),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            ),
+        ]
+
+    def _MEN_repo_splitter(self, filepath: Path) -> Dict:
+        articles = {}
+        entities = os.path.join(filepath, "MEN-Dataset-main/data/annotated_set.json")
+        relations = os.path.join(filepath, "MEN-Dataset-main/data/rel2id.json")
+
+        with open(entities, "r") as annot_json:
+            annots = json.load(annot_json)
+
+        article_ids = [i["id"] for i in annots]
+        for article_id in article_ids:
+            articles[article_id] = os.path.join(filepath, f"MEN-Dataset-main/data/article_text/{article_id}.txt")
+
+        data_dir = {"entities": entities, "articles": articles, "relations": relations}
+
+        return data_dir
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        filepath = self._MEN_repo_splitter(filepath)
+
+        with open(filepath["entities"], "r") as entities_json:
+            entities = json.load(entities_json)
+
+        articles = {}
+        for article_id in [i["id"] for i in entities]:
+            with open(filepath["articles"][article_id], "r") as article_txt:
+                article = article_txt.read()
+            articles[article_id] = article
+
+        i = 0
+        for item in entities:
+            article_id = item["id"]
+            entities = item["entities"]
+            relations = item["relations"]
+
+            i += 1
+            if self.config.schema == "source":
+                yield i, {
+                    "article": articles[article_id],
+                    "entities": [
+                        {
+                            "id": entity["id"],
+                            "label": entity["label"],
+                            "position": {
+                                "start": entity["position"]["start_offset"],
+                                "end": entity["position"]["end_offset"],
+                            },
+                        }
+                        for entity in entities
+                    ],
+                    "relations": [{"id": relation["id"], "head": relation["head"], "tail": relation["tail"], "relation": relation["relation"], "relation_source": relation["relation_source"]} for relation in relations],
+                }
+
+            elif self.config.schema == "seacrowd_kb":
+                yield i, {
+                    "id": str(i),
+                    "passages": [{"id": article_id, "type": "text", "text": [articles[article_id]], "offsets": [[0, len(articles[article_id])]]}],
+                    "entities": [
+                        {
+                            "id": f"{article_id}-entity-{entity['id']}",
+                            "type": entity["label"],
+                            "text": [articles[article_id][entity["position"]["start_offset"]:entity["position"]["end_offset"]]],
+                            "offsets": [[entity["position"]["start_offset"], entity["position"]["end_offset"]]],
+                            "normalized": [],
+                        }
+                        for entity in entities
+                    ],
+                    "events": [],
+                    "coreferences": [],
+                    "relations": [
+                        {
+                            "id": f"{article_id}-relation-{relation['id']}",
+                            "type": relation["relation"],
+                            "arg1_id": f"{article_id}-entity-{relation['head']}",
+                            "arg2_id": f"{article_id}-entity-{relation['tail']}",
+                            "normalized": [{"db_name": relation["relation_source"], "db_id": ""}],
+                        }
+                        for relation in relations
+                    ],
+                }