自然语言处理 | 使用Spacy 进行自然语言处理（二）

上次我们简单介绍了Spacy，学习了它的安装以及实体识别等基本的方法。今天我继续给大家介绍一下它的其他功能如何操作，主要有词性还原，词性标注，名词块识别，依存分析等内容。废话不多说，直接看代码。

1. import en_core_web_sm
3. parser = en_core_web_sm.load()
4. sentences = "There is an art, it says, or rather, a knack to flying." \
5. "The knack lies in learning how to throw yourself at the ground and miss." \
6. "In the beginning the Universe was created. This has made a lot of people " \
7. "very angry and been widely regarded as a bad move."
9. print("解析文本中包含的句子：")
10. sents = [sent for sent in parser(sentences).sents]
11. for x in sents:
12. print(x)
13. """
14. There is an art, it says, or rather, a knack to flying.
15. The knack lies in learning how to throw yourself at the ground and miss.
16. In the beginning the Universe was created.
17. This has made a lot of people very angry and been widely regarded as a bad move.
18. """
19. print("- * -"*20)
21. # 分词
22. print()
23. tokens = [token for token in sents[0] if len(token) > 1]
24. print(tokens)
25. print("- * -"*20)
27. # 词性还原
28. lemma_tokens = [token.lemma_ for token in sents[0] if len(token) > 1]
30. print(lemma_tokens)
31. print("- * -"*20)
33. # 简化版的词性标注
34. pos_tokens = [token.pos_ for token in sents[0] if len(token) > 1]
35. print(pos_tokens)
36. print("- * -"*20)
38. # 词性标注的细节版
39. tag_tokens = [token.tag_ for token in sents[0] if len(token) > 1]
40. print(tag_tokens)
41. print("- * -"*20)
43. # 依存分析
44. dep_tokens = [token.dep_ for token in sents[0] if len(token) > 1]
45. print(dep_tokens)
46. print("- * -"*20)
49. print("名词块分析")
50. doc = parser(u"Autonomous cars shift insurance liability toward manufacturers")
51. # 获取名词块文本
52. chunk_text = [chunk.text for chunk in doc.noun_chunks]
53. print(chunk_text)
54. print("- * -"*20)
56. # 获取名词块根结点的文本
57. chunk_root_text = [chunk.root.text for chunk in doc.noun_chunks]
58. print(chunk_root_text)
59. print("- * -"*20)
61. # 依存分析
62. chunk_root_dep_ = [chunk.root.dep_ for chunk in doc.noun_chunks]
63. print(chunk_root_dep_)
64. print("- * -"*20)
65. #
66. chunk_root_head_text = [chunk.root.head.text for chunk in doc.noun_chunks]
67. print(chunk_root_head_text)
68. print("- * -"*20)

最后给大家附上一个句法依存分析的结果解释的资料，是斯坦福自然语言处理的一个依存句法分析的解释文档

链接：https://nlp.stanford.edu/software/dependencies_manual.pdf

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