Update SemEval2015.py

添加使用nltk对text分词，并加入了ATESP_BIEOS_tags，ATESP_BIO_tags，ATE_BIEOS_tags，ATE_BIO_tags 4种标记方法。

SemEval2015.py

@@ -42,7 +42,7 @@ _CONFIG = [
     "laptops"
 ]
 
-_VERSION = "0.0.1"
+_VERSION = "0.1.0"
 
 _HOMEPAGE_URL = "https://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools/"
 _DOWNLOAD_URL = "https://raw.githubusercontent.com/YaxinCui/ABSADataset/main/SemEval2015Task12Corrected/{split}/{domain}_{split}.xml"
@@ -89,6 +89,12 @@ class SemEval2015(datasets.GeneratorBasedBuilder):
                     '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')
@@ -173,12 +179,98 @@ class SemEvalXMLDataset():
                 
             Opinions.sort(key=lambda x: x["from"])
             # 从小到大排序
-
-            self.SentenceWithOpinions.append({
+            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