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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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try: |
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from typing import TypedDict |
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except: |
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from typing_extensions import TypedDict |
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import datasets |
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from nusacrowd.nusa_datasets.indocoref.utils.text_preprocess import \ |
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TextPreprocess |
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from nusacrowd.utils import schemas |
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from nusacrowd.utils.configs import NusantaraConfig |
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from nusacrowd.utils.constants import Tasks |
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_CITATION = """\ |
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@inproceedings{artari-etal-2021-multi, |
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title = {A Multi-Pass Sieve Coreference Resolution for {I}ndonesian}, |
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author = {Artari, Valentina Kania Prameswara and Mahendra, Rahmad and Jiwanggi, Meganingrum Arista and Anggraito, Adityo and Budi, Indra}, |
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year = 2021, |
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month = sep, |
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booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)}, |
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publisher = {INCOMA Ltd.}, |
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address = {Held Online}, |
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pages = {79--85}, |
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url = {https://aclanthology.org/2021.ranlp-1.10}, |
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abstract = {Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74{\%} of MUC F-measure and 52.18{\%} of BCUBED F-measure.} |
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} |
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""" |
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_LOCAL = False |
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_LANGUAGES = ["ind"] |
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_DATASETNAME = "indocoref" |
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_DESCRIPTION = """\ |
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Dataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions: |
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- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films, |
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TV show episodes, and novel stories; (ii) biographies (incl. fictional characters); and (iii) historical events or important events. |
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- The pages contain significant variation of pronoun and named-entity. We count the number of first, second, third person pronouns, |
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and clitic pronouns in the document by applying string matching.We examine the number |
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of named-entity using the Stanford CoreNLP |
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NER Tagger (Manning et al., 2014) with a |
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model trained from the Indonesian corpus |
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taken from Alfina et al. (2016). |
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The Wikipedia texts have length of 500 to |
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2000 words. |
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We sample 201 of pages from subset of filtered |
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Wikipedia pages. We hire five annotators who are |
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undergraduate student in Linguistics department. |
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They are native in Indonesian. Annotation is carried out using the Script d’Annotation des Chanes |
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de Rfrence (SACR), a web-based Coreference resolution annotation tool developed by Oberle (2018). |
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From the 201 texts, there are 16,460 mentions |
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tagged by the annotators |
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""" |
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_HOMEPAGE = "https://github.com/valentinakania/indocoref/" |
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_LICENSE = "MIT" |
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_URLS = { |
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_DATASETNAME: "https://github.com/valentinakania/indocoref/archive/refs/heads/main.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.COREFERENCE_RESOLUTION] |
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_SOURCE_VERSION = "1.0.0" |
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_NUSANTARA_VERSION = "1.0.0" |
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class Indocoref(datasets.GeneratorBasedBuilder): |
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"""A collection of 210 curated articles from Wikipedia Bahasa Indonesia with Coreference Annotations""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION) |
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BUILDER_CONFIGS = [ |
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NusantaraConfig( |
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name="indocoref_source", |
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version=SOURCE_VERSION, |
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description="Indocoref source schema", |
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schema="source", |
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subset_id="indocoref", |
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), |
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NusantaraConfig( |
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name="indocoref_nusantara_kb", |
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version=NUSANTARA_VERSION, |
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description="Indocoref Nusantara schema", |
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schema="nusantara_kb", |
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subset_id="indocoref", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "indocoref_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("int64"), |
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"passage": datasets.Value("string"), |
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"mentions": [ |
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{ |
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"id": datasets.Value("int64"), |
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"labels": datasets.Sequence(datasets.Value("string")), |
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"class": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"pronoun": datasets.Value("bool"), |
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"proper": datasets.Value("bool"), |
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"sent": datasets.Value("int32"), |
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"cluster": datasets.Value("int32"), |
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"per": datasets.Value("bool"), |
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"org": datasets.Value("bool"), |
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"loc": datasets.Value("bool"), |
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"ner": datasets.Value("bool"), |
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} |
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], |
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} |
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) |
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elif self.config.schema == "nusantara_kb": |
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features = schemas.kb_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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class ReadPassage(TypedDict): |
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passage: str |
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annotated: str |
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mentions: List[any] |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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base_path = Path(dl_manager.download_and_extract(urls)) / "indocoref-main" / "data" |
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passage_path = base_path / "passage" |
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annotated_path = base_path / "annotated" |
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mentions_per_file = TextPreprocess(annotated_path).run(0) |
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data: List[self.ReadPassage] = [] |
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for passage_file_name, annotated_file_name in zip(sorted(os.listdir(passage_path)), sorted(os.listdir(annotated_path))): |
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passage_file_path, annotated_file_path = passage_path / passage_file_name, annotated_path / annotated_file_name |
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if os.path.isfile(passage_file_path) and os.path.isfile(annotated_file_path): |
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with open(passage_file_path, "r") as fpassage, open(annotated_file_path, "r") as fannotated: |
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data.append(self.ReadPassage(passage=fpassage.read(), annotated=fannotated.read(), mentions=mentions_per_file[annotated_file_name])) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data": data, |
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"split": "train", |
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}, |
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), |
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] |
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class DisjointSet: |
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parent = {} |
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def __init__(self, items): |
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for item in items: |
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self.parent[item] = item |
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def find(self, k): |
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if self.parent[k] == k: |
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return k |
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return self.find(self.parent[k]) |
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def union(self, a, b): |
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x = self.find(a) |
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y = self.find(b) |
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self.parent[x] = y |
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def _generate_examples(self, data: List[ReadPassage], split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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if self.config.schema == "source": |
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for index, example in enumerate(data): |
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passage, mentions = example["passage"], example["mentions"] |
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row = { |
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"id": index, |
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"passage": passage, |
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"mentions": [ |
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{ |
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"id": mention["id"], |
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"labels": mention["labels"], |
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"class": mention["class"], |
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"text": mention["text"], |
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"pronoun": mention["pronoun"], |
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"proper": mention["proper"], |
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"sent": mention["sent"], |
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"cluster": mention["cluster"], |
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"per": mention["per"], |
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"org": mention["org"], |
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"loc": mention["loc"], |
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"ner": mention["ner"], |
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} |
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for mention in mentions |
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], |
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} |
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yield index, row |
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elif self.config.schema == "nusantara_kb": |
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for index, example in enumerate(data): |
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passage, mentions = example["passage"], example["mentions"] |
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passage = passage.replace(" \n", " ") |
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passage = passage.replace("\n", " ") |
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all_labels = {label for mention in mentions for label in mention["labels"]} |
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labels_disjoint_set = self.DisjointSet(all_labels) |
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for mention in mentions: |
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for i in range(1, len(mention["labels"])): |
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labels_disjoint_set.union(mention["labels"][i], mention["labels"][i - 1]) |
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coreferences = {} |
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for mention in mentions: |
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coreference_id = labels_disjoint_set.find(mention["labels"][0]) |
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if coreference_id not in coreferences: |
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coreferences[coreference_id] = [] |
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coreferences[coreference_id].append(str(mention["id"])) |
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row_id = str(index) |
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row = { |
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"id": row_id, |
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"passages": [{"id": "passage-" + row_id, "type": "text", "text": [passage], "offsets": [[0, len(passage)]]}], |
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"entities": [ |
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{ |
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"id": row_id + "-entity-" + str(mention["id"]), |
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"type": mention["class"], |
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"text": [mention["text"]], |
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"offsets": [list(mention["offset"])], |
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"normalized": [], |
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} |
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for mention in mentions |
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], |
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"coreferences": [{"id": row_id + "-coreference-" + str(coref_id), "entity_ids": [row_id + "-entity-" + entity_id for entity_id in entity_ids]} for coref_id, entity_ids in enumerate(coreferences.values())], |
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"events": [], |
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"relations": [], |
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} |
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yield index, row |
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