Datasets:

Basvoju
/

SemEval2018Task7

	# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
	# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py


	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Semeval2018Task7 is a dataset that describes the first task on semantic relation extraction and classification in scientific paper abstracts"""



	import glob
	import datasets
	import xml.dom.minidom
	import xml.etree.ElementTree as ET

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{gabor-etal-2018-semeval,
	title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
	author = {G{\'a}bor, Kata and
	Buscaldi, Davide and
	Schumann, Anne-Kathrin and
	QasemiZadeh, Behrang and
	Zargayouna, Ha{\"\i}fa and
	Charnois, Thierry},
	booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
	month = jun,
	year = "2018",
	address = "New Orleans, Louisiana",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/S18-1111",
	doi = "10.18653/v1/S18-1111",
	pages = "679--688",
	abstract = "This paper describes the first task on semantic relation extraction and classification in
	scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations
	and includes three different subtasks. The subtasks were designed so as to compare and quantify the
	effect of different pre-processing steps on the relation classification results. We expect the task to
	be relevant for a broad range of researchers working on extracting specialized knowledge from domain
	corpora, for example but not limited to scientific or bio-medical information extraction. The task
	attracted a total of 32 participants, with 158 submissions across different scenarios.",
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	This paper describes the first task on semantic relation extraction and classification in scientific paper
	abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three
	different subtasks. The subtasks were designed so as to compare and quantify the effect of different
	pre-processing steps on the relation classification results. We expect the task to be relevant for a broad
	range of researchers working on extracting specialized knowledge from domain corpora, for example but not
	limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants,
	with 158 submissions across different scenarios.
	"""

	# Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/gkata/SemEval2018Task7/tree/testing"

	# Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"Subtask_1_1": {
	"train": {
	"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.relations.txt",
	"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.text.xml",
	},
	"test": {
	"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.relations.txt",
	"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.1.test.text.xml",
	},
	},
	"Subtask_1_2": {
	"train": {
	"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.relations.txt",
	"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.text.xml",
	},
	"test": {
	"relations": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.relations.txt",
	"text": "https://raw.githubusercontent.com/gkata/SemEval2018Task7/testing/1.2.test.text.xml",
	},
	},

	}


	def all_text_nodes(root):
	if root.text is not None:
	yield root.text
	for child in root:
	if child.tail is not None:
	yield child.tail


	def reading_entity_data(ET_data_to_convert):
	parsed_data = ET.tostring(ET_data_to_convert,"utf-8")
	parsed_data= parsed_data.decode('utf8').replace("b\'","")
	parsed_data= parsed_data.replace("<abstract>","")
	parsed_data= parsed_data.replace("</abstract>","")
	parsed_data= parsed_data.replace("<title>","")
	parsed_data= parsed_data.replace("</title>","")
	parsed_data = parsed_data.replace("\n\n\n","")

	parsing_tag = False
	final_string = ""
	tag_string= ""
	current_tag_id = ""
	current_tag_starting_pos = 0
	current_tag_ending_pos= 0
	entity_mapping_list=[]

	for i in parsed_data:
	if i=='<':
	parsing_tag = True
	if current_tag_id!="":
	current_tag_ending_pos = len(final_string)-1
	entity_mapping_list.append({"id":current_tag_id,
	"char_start":current_tag_starting_pos,
	"char_end":current_tag_ending_pos+1})
	current_tag_id= ""
	tag_string=""


	elif i=='>':
	parsing_tag = False
	tag_string_split = tag_string.split('"')
	if len(tag_string_split)>1:
	current_tag_id= tag_string.split('"')[1]
	current_tag_starting_pos = len(final_string)

	else:
	if parsing_tag!=True:
	final_string = final_string + i
	else:
	tag_string = tag_string + i

	return {"text_data":final_string, "entities":entity_mapping_list}



	class Semeval2018Task7(datasets.GeneratorBasedBuilder):
	"""
	Semeval2018Task7 is a dataset for semantic relation extraction and classification in scientific paper abstracts
	"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="Subtask_1_1", version=VERSION,
	description="Relation classification on clean data"),
	datasets.BuilderConfig(name="Subtask_1_2", version=VERSION,
	description="Relation classification on noisy data"),

	]
	DEFAULT_CONFIG_NAME = "Subtask_1_1"

	def _info(self):
	class_labels = ["","USAGE", "RESULT", "MODEL-FEATURE", "PART_WHOLE", "TOPIC", "COMPARE"]
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"entities": [
	{
	"id": datasets.Value("string"),
	"char_start": datasets.Value("int32"),
	"char_end": datasets.Value("int32")
	}
	],
	"relation": [
	{
	"label": datasets.ClassLabel(names=class_labels),
	"arg1": datasets.Value("string"),
	"arg2": datasets.Value("string"),
	"reverse": datasets.Value("bool")
	}
	]
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = _URLS[self.config.name]
	downloaded_files = dl_manager.download(urls)
	print(downloaded_files)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"relation_filepath": downloaded_files['train']["relations"],
	"text_filepath": downloaded_files['train']["text"],

	}

	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"relation_filepath": downloaded_files['test']["relations"],
	"text_filepath": downloaded_files['test']["text"],

	}

	)]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, relation_filepath, text_filepath):

	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(relation_filepath, encoding="utf-8") as f:
	relations = []
	text_id_to_relations_map= {}
	for key, row in enumerate(f):
	row_split = row.strip("\n").split("(")
	use_case = row_split[0]
	second_half = row_split[1].strip(")")
	second_half_splits = second_half.split(",")
	size = len(second_half_splits)

	relation = {
	"label": use_case,
	"arg1": second_half_splits[0],
	"arg2": second_half_splits[1],
	"reverse": True if size == 3 else False
	}
	relations.append(relation)

	arg_id = second_half_splits[0].split(".")[0]
	if arg_id not in text_id_to_relations_map:
	text_id_to_relations_map[arg_id] = [relation]
	else:
	text_id_to_relations_map[arg_id].append(relation)
	#print("result", text_id_to_relations_map)

	#for arg_id, values in text_id_to_relations_map.items():
	#print(f"ID: {arg_id}")
	# for value in values:
	# (value)



	doc2 = ET.parse(text_filepath)
	root = doc2.getroot()

	for child in root:
	if child.find("title")==None:
	continue
	text_id = child.attrib
	#print("text_id", text_id)

	if child.find("abstract")==None:
	continue
	title = child.find("title").text
	child_abstract = child.find("abstract")


	abstract_text_and_entities = reading_entity_data(child.find("abstract"))
	title_text_and_entities = reading_entity_data(child.find("title"))

	text_relations = []
	if text_id['id'] in text_id_to_relations_map:
	text_relations = text_id_to_relations_map[text_id['id']]

	yield text_id['id'], {
	"id": text_id['id'],
	"title": title_text_and_entities['text_data'],
	"abstract": abstract_text_and_entities['text_data'],
	"entities": abstract_text_and_entities['entities'] + title_text_and_entities['entities'],
	"relation": text_relations
	}