Beginning Natural Language Processing代码疑惑

在123页的pos-tagging with spaCy，我电脑上运行代码的结果和书上的不一致，原因未知。

1. 运行环境

    root@kali:~# ipython3
    Python 3.6.6 (default, Jun 27 2018, 14:44:17)
    Type "copyright", "credits" or "license" for more information.
   
    IPython 5.5.0 -- An enhanced Interactive Python.
    ?         -> Introduction and overview of IPython's features.
    %quickref -> Quick reference.
    help      -> Python's own help system.
    object?   -> Details about 'object', use 'object??' for extra details.
   
    In [16]: spacy.__version__
    Out[16]: '2.0.12'
   

2. 实际代码，只贴出不一致的两段代码
   书中的例子，sent_2中的refuse 在书中是’noun’, 而我的结果是refuse ADJ JJ,adjective形容词。
   sent_3中,her 在书中是ADJ, 形容词,　而我的结果是her PRON PRP, pronoun代词.
   第一个fish在书中是动词verb, 而我的结果是fish NOUN NN, noun 名词。

    In [4]: import spacy
   
    In [5]: nlp=spacy.load('en')
   
    In [6]: sent_0 = nlp('Mathiew and I went to the park.')
   
    In [7]: sent_1 = nlp('If Clement was asked to take out the garbage, he would ref
       ...: use.')
   
    In [8]: sent_2 = nlp('Baptiste was in charge of the refuse treatment center.')
   
    In [9]: sent_3 = nlp('Marie took out her rather suspicious and fishy cat to go f
       ...: ish for fish.')
   
   
    In [12]: for token in sent_2:
        ...: print(token.text, token.pos_, token.tag_)
        ...:
        ...:
    Baptiste PROPN NNP
    was VERB VBD
    in ADP IN
    charge NOUN NN
    of ADP IN
    the DET DT
    refuse ADJ JJ
    treatment NOUN NN
    center NOUN NN
    . PUNCT .
   
   
    In [15]: for token in sent_3:
        ...: print(token.text, token.pos_, token.tag_)
        ...:
    Marie PROPN NNP
    took VERB VBD
    out PART RP
    her PRON PRP
    rather ADV RB
    suspicious ADJ JJ
    and CCONJ CC
    fishy ADJ JJ
    cat NOUN NN
    to PART TO
    go VERB VB
    fish NOUN NN
    for ADP IN
    fish NOUN NN
    . PUNCT .