2.1 包含和统一

2.1 包含和统一

认为特征结构提供一些对象的部分信息是很正常的，在这个意义上，我们可以根据它们通用的程度给特征结构排序。例如，(23a)比(23b)具有更少特征，(23b)比(23c)具有更少特征。

[NUMBER = 74]

统一被正式定义为一个（部分）二元操作：FS0 ⊔ FS1。统一是对称的，所以 FS0 ⊔ FS1 = FS1 ⊔ FS0。在 Python 中也是如此：

>>> print(fs2.unify(fs1))
[ CITY   = 'Paris'      ]
[ NUMBER = 74           ]
[ STREET = 'rue Pascal' ]

如果我们统一两个具有包含关系的特征结构，那么统一的结果是两个中更具体的那个：

>>> fs0 = nltk.FeatStruct(A='a')
>>> fs1 = nltk.FeatStruct(A='b')
>>> fs2 = fs0.unify(fs1)
>>> print(fs2)
None

现在，如果我们看一下统一如何与结构共享相互作用，事情就变得很有趣。首先，让我们在 Python 中定义(21)：

>>> fs0 = nltk.FeatStruct("""[NAME=Lee,
...                           ADDRESS=[NUMBER=74,
...                                    STREET='rue Pascal'],
...                           SPOUSE= [NAME=Kim,
...                                    ADDRESS=[NUMBER=74,
...                                             STREET='rue Pascal']]]""")
>>> print(fs0)
[ ADDRESS = [ NUMBER = 74           ]               ]
[           [ STREET = 'rue Pascal' ]               ]
[                                                   ]
[ NAME    = 'Lee'                                   ]
[                                                   ]
[           [ ADDRESS = [ NUMBER = 74           ] ] ]
[ SPOUSE  = [           [ STREET = 'rue Pascal' ] ] ]
[           [                                     ] ]
[           [ NAME    = 'Kim'                     ] ]

我们为 Kim 的地址指定一个CITY作为参数会发生什么？请注意，fs1需要包括从特征结构的根到CITY的整个路径。

>>> fs1 = nltk.FeatStruct("[SPOUSE = [ADDRESS = [CITY = Paris]]]")
>>> print(fs1.unify(fs0))
[ ADDRESS = [ NUMBER = 74           ]               ]
[           [ STREET = 'rue Pascal' ]               ]
[                                                   ]
[ NAME    = 'Lee'                                   ]
[                                                   ]
[           [           [ CITY   = 'Paris'      ] ] ]
[           [ ADDRESS = [ NUMBER = 74           ] ] ]
[ SPOUSE  = [           [ STREET = 'rue Pascal' ] ] ]
[           [                                     ] ]
[           [ NAME    = 'Kim'                     ] ]

通过对比，如果fs1与fs2的结构共享版本统一，结果是非常不同的（如图(22)所示）：

>>> fs2 = nltk.FeatStruct("""[NAME=Lee, ADDRESS=(1)[NUMBER=74, STREET='rue Pascal'],
...                           SPOUSE=[NAME=Kim, ADDRESS->(1)]]""")
>>> print(fs1.unify(fs2))
[               [ CITY   = 'Paris'      ] ]
[ ADDRESS = (1) [ NUMBER = 74           ] ]
[               [ STREET = 'rue Pascal' ] ]
[                                         ]
[ NAME    = 'Lee'                         ]
[                                         ]
[ SPOUSE  = [ ADDRESS -> (1)  ]           ]
[           [ NAME    = 'Kim' ]           ]

不是仅仅更新 Kim 的 Lee 的地址的“副本”，我们现在同时更新他们两个的地址。更一般的，如果统一包含指定一些路径π的值，那么统一同时更新等价于π的任何路径的值。

正如我们已经看到的，结构共享也可以使用变量表示，如?x。

>>> fs1 = nltk.FeatStruct("[ADDRESS1=[NUMBER=74, STREET='rue Pascal']]")
>>> fs2 = nltk.FeatStruct("[ADDRESS1=?x, ADDRESS2=?x]")
>>> print(fs2)
[ ADDRESS1 = ?x ]
[ ADDRESS2 = ?x ]
>>> print(fs2.unify(fs1))
[ ADDRESS1 = (1) [ NUMBER = 74           ] ]
[                [ STREET = 'rue Pascal' ] ]
[                                          ]
[ ADDRESS2 -> (1)                          ]