使用 Huggingface Transformer 进行命名实体识别，映射回完整实体

Question

我正在查看用于命名实体识别的 Huggingface 管道的文档，我不清楚这些结果如何用于实际的实体识别模型。

例如，给出文档中的示例：

>>> from transformers import pipeline

>>> nlp = pipeline("ner")

>>> sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very"
...            "close to the Manhattan Bridge which is visible from the window."

This outputs a list of all words that have been identified as an entity from the 9 classes     defined above. Here is the expected results:

print(nlp(sequence))

[
{'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'},
{'word': '##gging', 'score': 0.9915938973426819, 'entity': 'I-ORG'},
{'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG'},
{'word': 'Inc', 'score': 0.9994403719902039, 'entity': 'I-ORG'},
{'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC'},
{'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC'},
{'word': 'City', 'score': 0.9993864893913269, 'entity': 'I-LOC'},
{'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
{'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
{'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},
{'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC'},
{'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
]

虽然仅此一项就令人印象深刻，但我不清楚从以下位置获取“DUMBO”的正确方法：

{'word': 'D', 'score': 0.9825621843338013, 'entity': 'I-LOC'},
{'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'},
{'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'},

--- 甚至更干净的多重标记匹配，比如区分“纽约市”和“约克市”。

虽然我可以想象启发式方法，但根据您的输入将这些标记重新连接回正确标签的正确预期方法是什么？

Answer 1

该管道对象可以为你做的，当你的参数设置grouped_entities到True。

from transformers import pipeline

ner = pipeline("ner", grouped_entities=True)

sequence = "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very close to the Manhattan Bridge which is visible from the window."

output = ner(sequence)

print(output)

输出：

[{'entity_group': 'I-ORG', 'score': 0.9970663785934448, 'word': 'Hugging Face Inc'}
, {'entity_group': 'I-LOC', 'score': 0.9993778467178345, 'word': 'New York City'}
, {'entity_group': 'I-LOC', 'score': 0.9571147759755453, 'word': 'DUMBO'}
, {'entity_group': 'I-LOC', 'score': 0.9838141202926636, 'word': 'Manhattan Bridge'}
, {'entity_group': 'I-LOC', 'score': 0.9838141202926636, 'word': 'Manhattan Bridge'}]