SpaCy：仅匹配多个模式的第一个实例

Question

我想使用以下代码查找iphone x或iphone <digit>的模式：

import spacy
from spacy.matcher import Matcher

TEXTS = ['How to preorder the iPhone X', 'iPhone X is coming', 'Should I pay $1,000 for the iPhone X?', 'The iPhone 8 reviews are here', 'Your iPhone goes up to 11 today', 'I need a new phone! Any tips?']

# Create pattern to match 'iphone' and 'x', or 'iphone' and optional number
pattern1 = [{'LOWER': 'iphone'}, {'LOWER': 'x'}]
pattern2 = [{'LOWER': 'iphone'}, {'IS_DIGIT': True, 'OP': '?'}]

# Add patterns to the matcher
nlp = spacy.load('en')
matcher = Matcher(nlp.vocab)
matcher.add('GADGET', None, pattern1, pattern2)

TRAINING_DATA = []

for doc in nlp.pipe(TEXTS):
    # Match on the doc and create a list of matched spans
    spans = [doc[start:end] for match_id, start, end in matcher(doc)]
    # Get (start character, end character, label) tuples of matches
    entities = [(span.start_char, span.end_char, 'GADGET') for span in spans]    
    # Format the matches as a (doc.text, entities) tuple
    training_example = (doc.text, {'entities': entities})
    # Append the example to the training data
    TRAINING_DATA.append(training_example)

print(*TRAINING_DATA, sep='\n')  

输出为：

('How to preorder the iPhone X', {'entities': [(20, 28, 'GADGET'), (20, 26, 'GADGET')]})
('iPhone X is coming', {'entities': [(0, 8, 'GADGET'), (0, 6, 'GADGET')]})
('Should I pay $1,000 for the iPhone X?', {'entities': [(28, 36, 'GADGET'), (28, 34, 'GADGET')]})
('The iPhone 8 reviews are here', {'entities': [(4, 12, 'GADGET')]})
('Your iPhone goes up to 11 today', {'entities': [(5, 11, 'GADGET')]})
('I need a new phone! Any tips?', {'entities': []})

您能否告诉我如何修改模式，以便获得此结果？

('How to preorder the iPhone X', {'entities': [(20, 28, 'GADGET')]})
('iPhone X is coming', {'entities': [(0, 8, 'GADGET')]})
('Should I pay $1,000 for the iPhone X?', {'entities': [(28, 36, 'GADGET')]})
('The iPhone 8 reviews are here', {'entities': [(4, 12, 'GADGET')]})
('Your iPhone goes up to 11 today', {'entities': [(5, 11, 'GADGET')]})
('I need a new phone! Any tips?', {'entities': []})

先感谢您。

Answer 1

解决方案是仅从字典entities的值列表中提取第一项。 也就是说，循环应为：

TRAINING_DATA = []

for doc in nlp.pipe(TEXTS):
    # Match on the doc and create a list of matched spans
    spans = [doc[start:end] for match_id, start, end in matcher(doc)]
    # Get (start character, end character, label) tuples of matches
    entities = [(span.start_char, span.end_char, 'GADGET') for span in spans]    
    # Format the matches as a (doc.text, entities) tuple
    training_example = (doc.text, {'entities': entities})
    # Append the example to the training data
    if len(entities) > 1:
        TRAINING_DATA.append((training_example[0], {'entities':entities[0]}))  
    else:
        TRAINING_DATA.append(training_example)