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id_coreference_resolution.py

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+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+from nusacrowd.utils.constants import Tasks
+from nusacrowd.utils import schemas
+
+import datasets
+import json
+import xml.etree.ElementTree as ET
+
+from nusacrowd.utils.configs import NusantaraConfig
+
+_CITATION = """\
+@INPROCEEDINGS{8074648,
+  author={Suherik, Gilang Julian and Purwarianti, Ayu},
+  booktitle={2017 5th International Conference on Information and Communication Technology (ICoIC7)}, 
+  title={Experiments on coreference resolution for Indonesian language with lexical and shallow syntactic features}, 
+  year={2017},
+  volume={},
+  number={},
+  pages={1-5},
+  doi={10.1109/ICoICT.2017.8074648}}
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_DATASETNAME = "id_coreference_resolution"
+
+_DESCRIPTION = """\
+We built Indonesian coreference resolution that solves not only pronoun referenced to proper noun, but also proper noun to proper noun and pronoun to pronoun.
+The differences with the available Indonesian coreference resolution lay on the problem scope and features. 
+We conducted experiments using various features (lexical and shallow syntactic features) such as appositive feature, nearest candidate feature, direct sentence feature, previous and next word feature, and a lexical feature of first person. 
+We also modified the method to build the training set by selecting the negative examples by cross pairing every single markable that appear between antecedent and anaphor. 
+Compared with two available methods to build the training set, we conducted experiments using C45 algorithm. 
+Using 200 news sentences, the best experiment achieved 71.6% F-Measure score.
+"""
+
+_HOMEPAGE = "https://github.com/tugas-akhir-nlp/indonesian-coreference-resolution-cnn/tree/master/data"
+
+_LICENSE = "Creative Commons Attribution-ShareAlike 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/tugas-akhir-nlp/indonesian-coreference-resolution-cnn/master/data/training/data.xml",
+        "test": "https://raw.githubusercontent.com/tugas-akhir-nlp/indonesian-coreference-resolution-cnn/master/data/testing/data.xml"
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.COREFERENCE_RESOLUTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+class IDCoreferenceResolution(datasets.GeneratorBasedBuilder):
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="id_coreference_resolution_source",
+            version=SOURCE_VERSION,
+            description="ID Coreference Resolution source schema",
+            schema="source",
+            subset_id="id_coreference_resolution",
+        ),
+        NusantaraConfig(
+            name="id_coreference_resolution_nusantara_kb",
+            version=NUSANTARA_VERSION,
+            description="ID Coreference Resolution Nusantara schema",
+            schema="nusantara_kb",
+            subset_id="id_coreference_resolution",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "id_coreference_resolution_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "phrases": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": [
+                                {
+                                    "word": datasets.Value("string"),
+                                    "ne": datasets.Value("string"),
+                                    "label": datasets.Value("string")
+                                }
+                            ]
+                        }
+                    ]
+                }
+            )
+
+        elif self.config.schema == "nusantara_kb":
+            features = schemas.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]:
+        urls = _URLS[_DATASETNAME]
+
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _parse_phrase(self, phrase):
+        splitted_text = phrase.text.split(" ")
+        splitted_ne = []
+        if ("ne" in phrase.attrib):
+            splitted_ne = phrase.attrib["ne"].split("|")
+        words = []
+        for i in range(0, len(splitted_text)):
+            word = splitted_text[i].split("\\")
+            ne = ""
+            label = ""
+            if (i < len(splitted_ne)):
+                ne = splitted_ne[i]
+            if (len(word) > 1):
+                label = word[1]
+            words.append({
+                "word": word[0],
+                "ne": ne,
+                "label": label
+            })
+        
+        id = ""
+
+        if ("id" in phrase.attrib):
+            id = phrase.attrib["id"]
+
+        return {
+            "id": id,
+            "type": phrase.attrib["type"],
+            "text": words
+        }
+
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        data = ET.parse(filepath).getroot()
+
+        for each_sentence in data:
+            sentence = {
+                "id": each_sentence.attrib["id"],
+                "phrases": [],
+            }
+            for phrase in each_sentence:
+                parsed_phrase = self._parse_phrase(phrase)
+                sentence["phrases"].append(parsed_phrase)
+
+            if self.config.schema == "source":
+                yield int(each_sentence.attrib["id"]), sentence
+
+            elif self.config.schema == "nusantara_kb":
+                ex = {
+                    "id": each_sentence.attrib["id"],
+                    "passages": [],
+                    "entities": [
+                        {
+                            "id": phrase["id"],
+                            "type": phrase["type"],
+                            "text": [text["word"] for text in phrase["text"]],
+                            "offsets": [[0, len(text["word"])] for text in phrase["text"]],
+                            "normalized": [{
+                                "db_name": text["ne"],
+                                "db_id": ""
+                            } for text in phrase["text"]],
+                        }
+                        for phrase in sentence["phrases"]
+                    ],
+                    "coreferences": [
+                        {
+                            "id": each_sentence.attrib["id"],
+                            "entity_ids": [phrase["id"] for phrase in sentence["phrases"]]
+                        }
+                    ],
+                    "events": [],
+                    "relations": [],
+                }
+                yield int(each_sentence.attrib["id"]), ex