2.7 未简化的标记

让我们找出每个名词类型中最频繁的名词。2.2中的程序找出所有以NN开始的标记，并为每个标记提供了几个示例单词。你会看到有许多NN的变种；最重要有此外，大多数的标记都有后缀修饰符：-NC表示引用，-HL表示标题中的词，-TL`表示标题（布朗标记的特征）。

  1. def findtags(tag_prefix, tagged_text):
  2.     cfd = nltk.ConditionalFreqDist((tag, word) for (word, tag) in tagged_text
  3.                                   if tag.startswith(tag_prefix))
  4.     return dict((tag, cfd[tag].most_common(5)) for tag in cfd.conditions())
  6. >>> tagdict = findtags('NN', nltk.corpus.brown.tagged_words(categories='news'))
  7. >>> for tag in sorted(tagdict):
  8. ...     print(tag, tagdict[tag])
  9. ...
  10. NN [('year', 137), ('time', 97), ('state', 88), ('week', 85), ('man', 72)]
  11. NN$ [("year's", 13), ("world's", 8), ("state's", 7), ("nation's", 6), ("company's", 6)]
  12. NN$-HL [("Golf's", 1), ("Navy's", 1)]
  13. NN$-TL [("President's", 11), ("Army's", 3), ("Gallery's", 3), ("University's", 3), ("League's", 3)]
  14. NN-HL [('sp.', 2), ('problem', 2), ('Question', 2), ('business', 2), ('Salary', 2)]
  15. NN-NC [('eva', 1), ('aya', 1), ('ova', 1)]
  16. NN-TL [('President', 88), ('House', 68), ('State', 59), ('University', 42), ('City', 41)]
  17. NN-TL-HL [('Fort', 2), ('Dr.', 1), ('Oak', 1), ('Street', 1), ('Basin', 1)]
  18. NNS [('years', 101), ('members', 69), ('people', 52), ('sales', 51), ('men', 46)]
  19. NNS$ [("children's", 7), ("women's", 5), ("janitors'", 3), ("men's", 3), ("taxpayers'", 2)]
  20. NNS$-HL [("Dealers'", 1), ("Idols'", 1)]
  21. NNS$-TL [("Women's", 4), ("States'", 3), ("Giants'", 2), ("Bros.'", 1), ("Writers'", 1)]
  22. NNS-HL [('comments', 1), ('Offenses', 1), ('Sacrifices', 1), ('funds', 1), ('Results', 1)]
  23. NNS-TL [('States', 38), ('Nations', 11), ('Masters', 10), ('Rules', 9), ('Communists', 9)]
  24. NNS-TL-HL [('Nations', 1)]

当我们开始在本章后续部分创建词性标注器时，我们将使用未简化的标记。