Update SemEval2016.py

SemEval2016.py

@@ -54,31 +54,31 @@ _CONFIG = [
     "digitalcameras_chinese"
 ]
 
-_VERSION = "0.0.1"
+_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 SemEval2016MultiConfig(datasets.BuilderConfig):
-    """BuilderConfig for SemEval2016MultiConfig."""
+class SemEval2016Config(datasets.BuilderConfig):
+    """BuilderConfig for SemEval2016Config."""
 
     def __init__(self, _CONFIG, **kwargs):
-        super(SemEval2016MultiConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
+        super(SemEval2016Config, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
         self.configs = _CONFIG
 
 
-class SemEval2016Multi(datasets.GeneratorBasedBuilder):
-    """The Multilingual Amazon Reviews Corpus"""
+class SemEval2016(datasets.GeneratorBasedBuilder):
+    """The Multilingual SemEval2016 ABSA Corpus"""
 
     BUILDER_CONFIGS = [
-        SemEval2016MultiConfig(
+        SemEval2016Config(
             name="All",
             _CONFIG=_CONFIG,
             description="A collection of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis.",
         )
     ] + [
-        SemEval2016MultiConfig(
+        SemEval2016Config(
             name=config,
             _CONFIG=[config],
             description=f"{config} of SemEval2016 specifically designed to aid research in multilingual Aspect Based Sentiment Analysis",
@@ -86,7 +86,7 @@ class SemEval2016Multi(datasets.GeneratorBasedBuilder):
         for config in _CONFIG
     ]
     
-    BUILDER_CONFIG_CLASS = SemEval2016MultiConfig
+    BUILDER_CONFIG_CLASS = SemEval2016Config
     DEFAULT_CONFIG_NAME = "All"
 
     def _info(self):
@@ -101,7 +101,12 @@ class SemEval2016Multi(datasets.GeneratorBasedBuilder):
                     'target': datasets.Value(dtype='string'),
                     'to': datasets.Value(dtype='string')}
                 ],
-                'language': 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')
@@ -193,13 +198,97 @@ class SemEvalXMLDataset():
                 
             Opinions.sort(key=lambda x: x["from"])
             # 从小到大排序
-
-            self.SentenceWithOpinions.append({
+            example = {
                     "text": text, 
                     "opinions": Opinions, 
-                    "language": language, 
                     "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