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SemEval2014Task4NLTK.py

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+# coding=utf-8
+# Copyright 2020 HuggingFace Datasets Authors.
+#
+# 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.
+
+"""The lingual SemEval2014 Task5 Reviews Corpus"""
+
+import datasets
+
+_CITATION = """\
+@article{2014SemEval,
+  title={SemEval-2014 Task 4: Aspect Based Sentiment Analysis},
+  author={ Pontiki, M.  and D Galanis and  Pavlopoulos, J.  and  Papageorgiou, H.  and  Manandhar, S. },
+  journal={Proceedings of International Workshop on Semantic Evaluation at},
+  year={2014},
+}
+"""
+
+_LICENSE = """\
+    Please click on the homepage URL for license details.
+"""
+
+_DESCRIPTION = """\
+A collection of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis.
+"""
+
+_CONFIG = [
+    
+    # restaurants domain
+    "restaurants",
+    # laptops domain
+    "laptops",
+]
+
+_VERSION = "0.0.1"
+
+_HOMEPAGE_URL = "https://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools"
+_DOWNLOAD_URL = "https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2014Task4/{split}/{domain}_{split}.xml"
+
+
+class SemEval2014Task4NLTKConfig(datasets.BuilderConfig):
+    """BuilderConfig for SemEval2014Config."""
+
+    def __init__(self, _CONFIG, **kwargs):
+        super(SemEval2014Task4NLTKConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
+        self.configs = _CONFIG
+
+
+class SemEval2014Task4NLTK(datasets.GeneratorBasedBuilder):
+    """The lingual Amazon Reviews Corpus"""
+
+    BUILDER_CONFIGS = [
+        SemEval2014Task4NLTKConfig(
+            name="All",
+            _CONFIG=_CONFIG,
+            description="A collection of SemEval2014 specifically designed to aid research in lingual Aspect Based Sentiment Analysis.",
+        )
+    ] + [
+        SemEval2014Task4NLTKConfig(
+            name=config,
+            _CONFIG=[config],
+            description=f"{config} of SemEval2014 specifically designed to aid research in Aspect Based Sentiment Analysis",
+        )
+        for config in _CONFIG
+    ]
+    
+    BUILDER_CONFIG_CLASS = SemEval2014Task4NLTKConfig
+    DEFAULT_CONFIG_NAME = "All"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {'text': datasets.Value(dtype='string'),
+                'aspectTerms': [
+                    {'from': datasets.Value(dtype='string'),
+                    'polarity': datasets.Value(dtype='string'),
+                    'term': datasets.Value(dtype='string'),
+                    'to': datasets.Value(dtype='string')}
+                ],
+                'tokens': [datasets.Value(dtype='string')],
+                'ATESP_BIEOS_tags': [datasets.Value(dtype='string')],
+                'ATESP_BIO_tags': [datasets.Value(dtype='string')],
+                'ATE_BIEOS_tags': [datasets.Value(dtype='string')],
+                'ATE_BIO_tags': [datasets.Value(dtype='string')],
+
+                'aspectCategories': [
+                    {'category': datasets.Value(dtype='string'),
+                    'polarity': datasets.Value(dtype='string')}
+                ],
+                'domain': datasets.Value(dtype='string'),
+                'sentenceId': datasets.Value(dtype='string')
+            }
+            ),
+            supervised_keys=None,
+            license=_LICENSE,
+            homepage=_HOMEPAGE_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):        
+
+        train_urls = [_DOWNLOAD_URL.format(split="train", domain=config) for config in self.config.configs]
+        dev_urls = [_DOWNLOAD_URL.format(split="trial", domain=config) for config in self.config.configs]
+        test_urls = [_DOWNLOAD_URL.format(split="test", domain=config) for config in self.config.configs]
+
+        train_paths = dl_manager.download_and_extract(train_urls)
+        dev_paths = dl_manager.download_and_extract(dev_urls)
+        test_paths = dl_manager.download_and_extract(test_urls)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths, "domain_list": self.config.configs}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths, "domain_list": self.config.configs}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths, "domain_list": self.config.configs}),
+        ]
+
+    def _generate_examples(self, file_paths, domain_list):
+        row_count = 0
+        assert len(file_paths)==len(domain_list)
+
+        for i in range(len(file_paths)):
+            file_path, domain = file_paths[i], domain_list[i]
+            semEvalDataset = SemEvalXMLDataset(file_path, domain)
+
+            for example in semEvalDataset.SentenceWithOpinions:
+                yield row_count, example
+                row_count += 1
+
+from xml.dom.minidom import parse
+
+class SemEvalXMLDataset():
+    def __init__(self, file_name, domain):
+        # 获得SentenceWithOpinions，一个List包含(reviewId, sentenceId, text, Opinions)
+
+        self.SentenceWithOpinions = []
+        self.xml_path = file_name
+
+        self.sentenceXmlList = parse(self.xml_path).getElementsByTagName('sentence')
+
+        for sentenceXml in self.sentenceXmlList:
+            
+            sentenceId = sentenceXml.getAttribute("id")
+            if len(sentenceXml.getElementsByTagName("text")[0].childNodes) < 1:
+                # skip no reviews part
+                continue
+            text = sentenceXml.getElementsByTagName("text")[0].childNodes[0].nodeValue
+
+            aspectTermsXLMList = sentenceXml.getElementsByTagName("aspectTerm")
+            aspectTerms = []
+            for opinionXml in aspectTermsXLMList:
+                # some text maybe have no opinion
+                term = opinionXml.getAttribute("term")
+                polarity = opinionXml.getAttribute("polarity")
+                from_ = opinionXml.getAttribute("from")
+                to = opinionXml.getAttribute("to")
+                aspectTermDict = {
+                    "term": term,
+                    "polarity": polarity,
+                    "from": from_,
+                    "to": to
+                }
+                aspectTerms.append(aspectTermDict)
+
+            # 从小到大排序
+            aspectTerms.sort(key=lambda x: int(x["from"]))
+
+            aspectCategoriesXmlList = sentenceXml.getElementsByTagName("aspectCategory")
+            aspectCategories = []
+            for aspectCategoryXml in aspectCategoriesXmlList:
+                category = aspectCategoryXml.getAttribute("category")
+                polarity = aspectCategoryXml.getAttribute("polarity")
+                aspectCategoryDict = {
+                    "category": category,
+                    "polarity": polarity
+                }
+                aspectCategories.append(aspectCategoryDict)
+            
+            example = {
+                    "text": text, 
+                    "aspectTerms": aspectTerms,
+                    "aspectCategories": aspectCategories,
+                    "domain": domain, 
+                    "sentenceId": sentenceId
+                }
+            example = addTokenAndLabel(example)
+            self.SentenceWithOpinions.append(example)
+
+import nltk
+
+def clearOpinion(example):
+    opinions = example['aspectTerms']
+    skipNullOpinions = []
+    # 去掉NULL的opinion
+    for opinion in opinions:
+        targetKey = 'term'
+        target = opinion[targetKey]
+        from_ = opinion['from']
+        to = opinion['to']
+        # skill NULL
+        if target.lower() == 'null' or target == '' or from_ == to:
+            continue
+        skipNullOpinions.append(opinion)
+        
+    # delete repeate Opinions
+    skipNullOpinions.sort(key=lambda x: int(x['from'])) # 从小到大排序
+    UniOpinions = []
+    for opinion in skipNullOpinions:
+        if len(UniOpinions) < 1:
+            UniOpinions.append(opinion)
+        else:
+            if opinion['from'] != UniOpinions[-1]['from'] and opinion['to'] != UniOpinions[-1]['to']:
+                UniOpinions.append(opinion)
+    return UniOpinions
+    
+
+def addTokenAndLabel(example):
+    tokens = []
+    labels = []
+    
+    text = example['text']
+    # UniOpinions = clearOpinion(example)
+    UniOpinions = example['aspectTerms']
+    text_begin = 0
+    
+    for aspect in UniOpinions:
+        polarity = aspect['polarity'][:3].upper()
+        pre_O_tokens = nltk.word_tokenize(text[text_begin: int(aspect['from'])])
+        tokens.extend(pre_O_tokens)
+        labels.extend(['O']*len(pre_O_tokens))
+        
+        BIES_tokens = nltk.word_tokenize(text[int(aspect['from']): int(aspect['to'])])
+        tokens.extend(BIES_tokens)
+        
+        assert len(BIES_tokens) > 0, print('error in BIES_tokens length')
+
+        if len(BIES_tokens)==1:
+            labels.append('S-'+polarity)
+        elif len(BIES_tokens)==2:
+            labels.append('B-'+polarity)
+            labels.append('E-'+polarity)
+        else:
+            labels.append('B-'+polarity)
+            labels.extend(['I-'+polarity]*(len(BIES_tokens)-2))
+            labels.append('E-'+polarity)
+
+        text_begin = int(aspect['to'])
+    
+
+    pre_O_tokens = nltk.word_tokenize(text[text_begin: ])
+    labels.extend(['O']*len(pre_O_tokens))
+    tokens.extend(pre_O_tokens)
+
+    example['tokens'] = tokens
+    example['ATESP_BIEOS_tags'] = labels
+    
+    
+    ATESP_BIO_labels = []
+    for label in labels:
+        ATESP_BIO_labels.append(label.replace('E-', 'I-').replace('S-', 'B-'))
+    example['ATESP_BIO_tags'] = ATESP_BIO_labels
+    
+    
+    ATE_BIEOS_labels = []
+    for label in labels:
+        ATE_BIEOS_labels.append(label[0])
+    example['ATE_BIEOS_tags'] = ATE_BIEOS_labels
+
+    ATE_BIO_labels = []
+    for label in ATESP_BIO_labels:
+        ATE_BIO_labels.append(label[0])
+    example['ATE_BIO_tags'] = ATE_BIO_labels
+
+    return example