Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
entity-linking-classification
Languages:
English
Size:
< 1K
License:
| # 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 | |
| } | |