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SemEval2016Task5NLTK.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 Multilingual SemEval2016 Task5 Reviews Corpus"""
+
+import datasets
+
+_CITATION = """\
+@inproceedings{pontiki2016semeval,
+  title={Semeval-2016 task 5: Aspect based sentiment analysis},
+  author={Pontiki, Maria and Galanis, Dimitrios and Papageorgiou, Haris and Androutsopoulos, Ion and Manandhar, Suresh and Al-Smadi, Mohammad and Al-Ayyoub, Mahmoud and Zhao, Yanyan and Qin, Bing and De Clercq, Orph{\'e}e and others},
+  booktitle={International workshop on semantic evaluation},
+  pages={19--30},
+  year={2016}
+}
+"""
+
+_LICENSE = """\
+    Please click on the homepage URL for license details.
+"""
+
+_DESCRIPTION = """\
+A collection of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis.
+"""
+
+_CONFIG = [
+    # restaruants Domain
+    "restaurants_english",
+    "restaurants_french",
+    "restaurants_spanish",
+    "restaurants_russian",
+    "restaurants_dutch",
+    "restaurants_turkish",
+    
+    # hotels domain
+    "hotels_arabic",
+
+    # Consumer Electronics Domain
+    "mobilephones_dutch",
+    "mobilephones_chinese",
+    "laptops_english",
+    "digitalcameras_chinese"
+]
+
+_VERSION = "0.1.0"
+
+_HOMEPAGE_URL = "https://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools/"
+_DOWNLOAD_URL = "https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2016Task5Corrected/{split}/{domain}_{split}_{lang}.xml"
+
+
+class SemEval2016Task5NLTKConfig(datasets.BuilderConfig):
+    """BuilderConfig for SemEval2016Config."""
+
+    def __init__(self, _CONFIG, **kwargs):
+        super(SemEval2016Task5NLTKConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
+        self.configs = _CONFIG
+
+
+class SemEval2016Task5NLTK(datasets.GeneratorBasedBuilder):
+    """The Multilingual SemEval2016 ABSA Corpus"""
+
+    BUILDER_CONFIGS = [
+        SemEval2016Task5NLTKConfig(
+            name="All",
+            _CONFIG=_CONFIG,
+            description="A collection of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis.",
+        )
+    ] + [
+        SemEval2016Task5NLTKConfig(
+            name=config,
+            _CONFIG=[config],
+            description=f"{config} of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis",
+        )
+        for config in _CONFIG
+    ]
+    
+    BUILDER_CONFIG_CLASS = SemEval2016Task5NLTKConfig
+    DEFAULT_CONFIG_NAME = "restaurants_english"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {'text': datasets.Value(dtype='string'),
+                'opinions': [
+                    {'category': datasets.Value(dtype='string'),
+                    'from': datasets.Value(dtype='string'),
+                    'polarity': datasets.Value(dtype='string'),
+                    'target': 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')],
+
+                'domain': datasets.Value(dtype='string'),
+                'reviewId': 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):
+        
+        lang_list = []
+        domain_list = []
+        
+        for config in self.config.configs:
+            domain_list.append(config.split('_')[0])
+            lang_list.append(config.split('_')[1])
+
+        train_urls = [_DOWNLOAD_URL.format(split="train", domain=config.split('_')[0], lang=config.split('_')[1]) for config in self.config.configs]
+        dev_urls = [_DOWNLOAD_URL.format(split="trial", domain=config.split('_')[0], lang=config.split('_')[1]) for config in self.config.configs]
+        test_urls = [_DOWNLOAD_URL.format(split="test", domain=config.split('_')[0], lang=config.split('_')[1]) 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, "lang_list": lang_list, "domain_list": domain_list}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"file_paths": dev_paths, "lang_list": lang_list, "domain_list": domain_list}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths, "lang_list": lang_list, "domain_list": domain_list}),
+        ]
+
+    def _generate_examples(self, file_paths, lang_list, domain_list):
+        row_count = 0
+        assert len(file_paths)==len(lang_list) and len(lang_list)==len(domain_list)
+
+        for i in range(len(file_paths)):
+            file_path, domain, language = file_paths[i], domain_list[i], lang_list[i]
+            semEvalDataset = SemEvalXMLDataset(file_path, language, domain)
+
+            for example in semEvalDataset.SentenceWithOpinions:
+
+                yield row_count, example
+                row_count += 1
+
+
+# 输入：xlm文件的文件路径
+# 输出：一个DataSet，每个样例包含[reviewid, sentenceId, text, UniOpinions]
+#      每个样例包含的Opinion，是一个列表，包含的是单个Opinion的详情
+
+from xml.dom.minidom import parse
+
+class SemEvalXMLDataset():
+    def __init__(self, file_name, language, 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:
+            reviewId = sentenceXml.getAttribute("id").split(':')[0]
+            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
+            OpinionXmlList = sentenceXml.getElementsByTagName("Opinion")
+            Opinions = []
+            for opinionXml in OpinionXmlList:
+                # some text maybe have no opinion
+                target = opinionXml.getAttribute("target")
+                category = opinionXml.getAttribute("category")
+                polarity = opinionXml.getAttribute("polarity")
+                from_ = opinionXml.getAttribute("from")
+                to = opinionXml.getAttribute("to")
+
+                opinionDict = {
+                    "target": target,
+                    "category": category,
+                    "polarity": polarity,
+                    "from": from_,
+                    "to": to
+                }
+                Opinions.append(opinionDict)
+                
+            Opinions.sort(key=lambda x: x["from"])
+            # 从小到大排序
+            example = {
+                    "text": text, 
+                    "opinions": Opinions, 
+                    "domain": domain, 
+                    "reviewId": reviewId, 
+                    "sentenceId": sentenceId
+                }
+            example = addTokenAndLabel(example)
+            self.SentenceWithOpinions.append(example)
+
+import nltk
+
+def clearOpinion(example):
+    opinions = example['opinions']
+    skipNullOpinions = []
+    # 去掉NULL的opinion
+    for opinion in opinions:
+        targetKey = 'target'
+        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)
+    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