|
import os |
|
from pathlib import Path |
|
from typing import Dict, List, Tuple |
|
from seacrowd.utils.constants import Tasks |
|
from seacrowd.utils import schemas |
|
|
|
import datasets |
|
import json |
|
import xml.etree.ElementTree as ET |
|
|
|
from seacrowd.utils.configs import SEACrowdConfig |
|
|
|
_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"] |
|
_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" |
|
|
|
_SEACROWD_VERSION = "2024.06.20" |
|
|
|
class IDCoreferenceResolution(datasets.GeneratorBasedBuilder): |
|
|
|
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
|
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
|
|
|
BUILDER_CONFIGS = [ |
|
SEACrowdConfig( |
|
name="id_coreference_resolution_source", |
|
version=SOURCE_VERSION, |
|
description="ID Coreference Resolution source schema", |
|
schema="source", |
|
subset_id="id_coreference_resolution", |
|
), |
|
SEACrowdConfig( |
|
name="id_coreference_resolution_seacrowd_kb", |
|
version=SEACROWD_VERSION, |
|
description="ID Coreference Resolution Nusantara schema", |
|
schema="seacrowd_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 == "seacrowd_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 == "seacrowd_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 |
|
|