# 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. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import xml.etree.ElementTree as ET import os import datasets from datasets import ClassLabel _CITATION = """\ @inproceedings{pontiki-etal-2014-semeval, title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis", author = "Pontiki, Maria and Galanis, Dimitris and Pavlopoulos, John and Papageorgiou, Harris and Androutsopoulos, Ion and Manandhar, Suresh", booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)", month = aug, year = "2014", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S14-2004", doi = "10.3115/v1/S14-2004", pages = "27--35", } """ _DESCRIPTION = """\ These are the datasets for Aspect Based Sentiment Analysis (ABSA), Task 4 of SemEval-2014. """ _HOMEPAGE = "https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: 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 = { "restaurants": {"trial": "restaurants-trial.xml", "train": "SemEval'14-ABSA-TrainData_v2 & AnnotationGuidelines/Restaurants_Train_v2.xml", "test": "ABSA_Gold_TestData/Restaurants_Test_Gold.xml"}, "laptops": {"trial": "laptops-trial.xml", "train": "SemEval'14-ABSA-TrainData_v2 & AnnotationGuidelines/Laptop_Train_v2.xml", "test": "ABSA_Gold_TestData/Laptops_Test_Gold.xml"}, } class SemEval2014Task4(datasets.GeneratorBasedBuilder): """These are the datasets for Aspect Based Sentiment Analysis (ABSA), Task 4 of SemEval-2014.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig(name="restaurants", version=VERSION, description="Restaurant review sentences"), datasets.BuilderConfig(name="laptops", version=VERSION, description="Laptop review sentences"), ] # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset if self.config.name == "restaurants": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( {'sentenceId': datasets.Value(dtype='string'), 'text': datasets.Value(dtype='string'), 'aspectTerms': [ {'term': datasets.Value(dtype='string'), # 'polarity': ClassLabel(num_classes=4, names=['positive', 'negative', 'neutral', 'conflict']), 'polarity': datasets.Value(dtype='string'), 'from': datasets.Value(dtype='string'), 'to': datasets.Value(dtype='string')} ], 'aspectCategories': [ # {'category': ClassLabel(num_classes=5, names=['food', 'service', 'price', 'ambience', 'anecdotes/miscellaneous']), # 'polarity': ClassLabel(num_classes=4, names=['positive', 'negative', 'neutral', 'conflict'])} {'category': datasets.Value(dtype='string'), 'polarity': datasets.Value(dtype='string')} ], # 'domain': ClassLabel(num_classes=2, names=['restaurants', 'laptops']) } ) elif self.config.name == "laptops": features = datasets.Features( {'sentenceId': datasets.Value(dtype='string'), 'text': datasets.Value(dtype='string'), 'aspectTerms': [ {'term': datasets.Value(dtype='string'), # 'polarity': ClassLabel(num_classes=4, names=['positive', 'negative', 'neutral', 'conflict']), 'polarity': datasets.Value(dtype='string'), 'from': datasets.Value(dtype='string'), 'to': datasets.Value(dtype='string')} ], # 'domain': ClassLabel(num_classes=2, names=['restaurants', 'laptops']) } ) 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): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # 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] data_dir = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split("trial"), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['trial'], "split": "trial" }, ), datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['train'], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir['test'], "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `id_` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. tree = ET.parse(filepath) root = tree.getroot() for id_, sentence in enumerate(root.iter("sentence")): sentenceId = sentence.attrib.get("id") text = sentence.find("text").text aspectTerms = [] for aspectTerm in sentence.iter("aspectTerm"): aspectTerms.append(aspectTerm.attrib) if self.config.name == "restaurants": aspectCategories = [] for aspectCategory in sentence.iter("aspectCategory"): aspectCategories.append(aspectCategory.attrib) yield id_, { "sentenceId": sentenceId, "text": text, "aspectTerms": aspectTerms, "aspectCategories": aspectCategories, # "domain": self.config.name, } elif self.config.name == 'laptops': yield id_, { "sentenceId": sentenceId, "text": text, "aspectTerms": aspectTerms, # "domain": self.config.name, }