Question

我正在尝试获取英语单词的基本英语单词，该单词是从其基本形式修改的。这个问题已在这里提出，但我没有看到正确的答案，所以我试图这样说。我尝试了两个来自NLTK包的词干器和一个词形变换器，它们是搬运器，干扰器，雪球器和wordnet lemmatiser。

我试过这段代码：

from nltk.stem.porter import PorterStemmer
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNetLemmatizer

words = ['arrival','conclusion','ate']

for word in words:
    print "\n\nOriginal Word =>", word
    print "porter stemmer=>", PorterStemmer().stem(word)
    snowball_stemmer = SnowballStemmer("english")
    print "snowball stemmer=>", snowball_stemmer.stem(word)
    print "WordNet Lemmatizer=>", WordNetLemmatizer().lemmatize(word)

这是我得到的输出：

Original Word => arrival
porter stemmer=> arriv
snowball stemmer=> arriv
WordNet Lemmatizer=> arrival


Original Word => conclusion
porter stemmer=> conclus
snowball stemmer=> conclus
WordNet Lemmatizer=> conclusion


Original Word => ate
porter stemmer=> ate
snowball stemmer=> ate
WordNet Lemmatizer=> ate

但我想要这个输出

    Input : arrival
    Output: arrive

    Input : conclusion
    Output: conclude

    Input : ate
    Output: eat

我怎样才能做到这一点？有没有可用的工具？这称为形态分析。我知道这一点，但必须有一些工具已经实现了这一点。感谢帮助：）

首次修改

我试过这段代码

import nltk
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from nltk.corpus import wordnet as wn

query = "The Indian economy is the worlds tenth largest by nominal GDP and third largest by purchasing power parity"

def is_noun(tag):
    return tag in ['NN', 'NNS', 'NNP', 'NNPS']

def is_verb(tag):
    return tag in ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']

def is_adverb(tag):
    return tag in ['RB', 'RBR', 'RBS']

def is_adjective(tag):
    return tag in ['JJ', 'JJR', 'JJS']

def penn_to_wn(tag):
    if is_adjective(tag):
        return wn.ADJ
    elif is_noun(tag):
        return wn.NOUN
    elif is_adverb(tag):
        return wn.ADV
    elif is_verb(tag):
        return wn.VERB
    return wn.NOUN

tags = nltk.pos_tag(word_tokenize(query))
for tag in tags:
    wn_tag = penn_to_wn(tag[1])
    print tag[0]+"---> "+WordNetLemmatizer().lemmatize(tag[0],wn_tag)

在这里，我尝试使用wordnet lemmatizer提供适当的标签。这是输出：

The---> The
Indian---> Indian
economy---> economy
is---> be
the---> the
worlds---> world
tenth---> tenth
largest---> large
by---> by
nominal---> nominal
GDP---> GDP
and---> and
third---> third
largest---> large
by---> by
purchasing---> purchase
power---> power
parity---> parity

仍然，像＆＃34;到达＆＃34;和＆＃34;结论＆＃34;不会用这种方法处理。对此有什么解决方案吗？

Answer 1

好的，所以...对于“ate”这个词，我认为你正在寻找NodeBox::Linguistics。

print en.verb.present("gave")
>>> give

我并不完全理解为什么你想要动词或“到达”而不是“结论”。

Answer 2

试用word_stemmer包，从here克隆并执行pip install -e word_forms。

from word_forms.word_forms import get_word_forms
get_word_forms('conclusion')

# gives:
{'a': {'conclusive'},
 'n': {'conclusion', 'conclusions', 'conclusivenesses', 'conclusiveness'},
 'r': {'conclusively'},
 'v': {'concludes', 'concluded', 'concluding', 'conclude'}}

在您的情况下，您希望从 n oun单词表单中获取 v erb表单。

获得英语单词的基本形式

2 个答案: