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 .