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SemEval2014Task4.py

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+# 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": {"train": "SemEval'14-ABSA-TrainData_v2 & AnnotationGuidelines/Restaurants_Train_v2.xml",
+                    "test": "ABSA_Gold_TestData/Restaurants_Test_Gold.xml"},
+    "laptops": {"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']),
+                    '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'])}
+                ],
+                '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']),
+                      '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.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,
+                }