| 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 |
| ], |
| } |
|
|
