将频率计数的意大利语句词解释

Question

我想对一些意大利文本进行词形推理，以便对单词进行频率计数并进一步调查此词形结构内容的输出。

我更喜欢lematizing而不是词干，因为我可以从句子中的上下文中提取词义（例如区分动词和名词）并获得语言中存在的词，而不是那些词的根源＃＆＃ 39; t通常有意义。

我发现这个名为pattern（pip2 install pattern）的库应该补充nltk，以便对意大利语进行词形还原，但我不是确保下面的方法是正确的，因为每个单词都是单独的，而不是在句子的上下文中。

可能我应该让pattern有责任对一个句子进行标记（所以也要用关于动词/名词/形容词等的元数据来注释每个单词），然后检索被词形化的单词，但我无法做到这一点我现在还不确定它是否可能？

另外：在意大利语中，一些文章用撇号进行渲染，例如＆＃34; l＆＃39; appartamento＆＃34; （英文＆＃34;公寓＆＃34;）实际上是2个字：＆＃34; lo＆＃34;和＆＃34; appartamento＆＃34;。现在我无法找到使用nltk和pattern组合分割这两个单词的方法，因此我无法以正确的方式计算单词的频率。

import nltk
import string
import pattern

# dictionary of Italian stop-words
it_stop_words = nltk.corpus.stopwords.words('italian')
# Snowball stemmer with rules for the Italian language
ita_stemmer = nltk.stem.snowball.ItalianStemmer()

# the following function is just to get the lemma
# out of the original input word (but right now
# it may be loosing the context about the sentence
# from where the word is coming from i.e.
# the same word could either be a noun/verb/adjective
# according to the context)
def lemmatize_word(input_word):
    in_word = input_word#.decode('utf-8')
    # print('Something: {}'.format(in_word))
    word_it = pattern.it.parse(
        in_word, 
        tokenize=False,  
        tag=False,  
        chunk=False,  
        lemmata=True 
    )
    # print("Input: {} Output: {}".format(in_word, word_it))
    the_lemmatized_word = word_it.split()[0][0][4]
    # print("Returning: {}".format(the_lemmatized_word))
    return the_lemmatized_word

it_string = "Ieri sono andato in due supermercati. Oggi volevo andare all'ippodromo. Stasera mangio la pizza con le verdure."

# 1st tokenize the sentence(s)
word_tokenized_list = nltk.tokenize.word_tokenize(it_string)
print("1) NLTK tokenizer, num words: {} for list: {}".format(len(word_tokenized_list), word_tokenized_list))

# 2nd remove punctuation and everything lower case
word_tokenized_no_punct = [string.lower(x) for x in word_tokenized_list if x not in string.punctuation]
print("2) Clean punctuation, num words: {} for list: {}".format(len(word_tokenized_no_punct), word_tokenized_no_punct))

# 3rd remove stop words (for the Italian language)
word_tokenized_no_punct_no_sw = [x for x in word_tokenized_no_punct if x not in it_stop_words]
print("3) Clean stop-words, num words: {} for list: {}".format(len(word_tokenized_no_punct_no_sw), word_tokenized_no_punct_no_sw))

# 4.1 lemmatize the words
word_tokenize_list_no_punct_lc_no_stowords_lemmatized = [lemmatize_word(x) for x in word_tokenized_no_punct_no_sw]
print("4.1) lemmatizer, num words: {} for list: {}".format(len(word_tokenize_list_no_punct_lc_no_stowords_lemmatized), word_tokenize_list_no_punct_lc_no_stowords_lemmatized))

# 4.2 snowball stemmer for Italian
word_tokenize_list_no_punct_lc_no_stowords_stem = [ita_stemmer.stem(i) for i in word_tokenized_no_punct_no_sw]
print("4.2) stemmer, num words: {} for list: {}".format(len(word_tokenize_list_no_punct_lc_no_stowords_stem), word_tokenize_list_no_punct_lc_no_stowords_stem))

# difference between stemmer and lemmatizer
print(
    "For original word(s) '{}' and '{}' the stemmer: '{}' '{}' (count 1 each), the lemmatizer: '{}' '{}' (count 2)"
    .format(
        word_tokenized_no_punct_no_sw[1],
        word_tokenized_no_punct_no_sw[6],
        word_tokenize_list_no_punct_lc_no_stowords_stem[1],
        word_tokenize_list_no_punct_lc_no_stowords_stem[6],
        word_tokenize_list_no_punct_lc_no_stowords_lemmatized[1],
        word_tokenize_list_no_punct_lc_no_stowords_lemmatized[1]
    )
)

给出这个输出：

1) NLTK tokenizer, num words: 20 for list: ['Ieri', 'sono', 'andato', 'in', 'due', 'supermercati', '.', 'Oggi', 'volevo', 'andare', "all'ippodromo", '.', 'Stasera', 'mangio', 'la', 'pizza', 'con', 'le', 'verdure', '.']
2) Clean punctuation, num words: 17 for list: ['ieri', 'sono', 'andato', 'in', 'due', 'supermercati', 'oggi', 'volevo', 'andare', "all'ippodromo", 'stasera', 'mangio', 'la', 'pizza', 'con', 'le', 'verdure']
3) Clean stop-words, num words: 12 for list: ['ieri', 'andato', 'due', 'supermercati', 'oggi', 'volevo', 'andare', "all'ippodromo", 'stasera', 'mangio', 'pizza', 'verdure']
4.1) lemmatizer, num words: 12 for list: [u'ieri', u'andarsene', u'due', u'supermercato', u'oggi', u'volere', u'andare', u"all'ippodromo", u'stasera', u'mangiare', u'pizza', u'verdura']
4.2) stemmer, num words: 12 for list: [u'ier', u'andat', u'due', u'supermerc', u'oggi', u'vol', u'andar', u"all'ippodrom", u'staser', u'mang', u'pizz', u'verdur']
For original word(s) 'andato' and 'andare' the stemmer: 'andat' 'andar' (count 1 each), the lemmatizer: 'andarsene' 'andarsene' (count 2)

• 如何使用{token}使用pattern有效地对某些句子进行词形变换？ （假设lemmas被识别为名词/动词/形容词等。）
• pattern是否有nltk的python替代词用于guard的意大利语词形还原？
• 如何使用撇号分割绑定到下一个单词的文章？

Answer 1

我会尝试回答你的问题，因为我知道我对意大利语知之甚少！

1）据我所知，删除撇号的主要责任是标记器，因此nltk意大利语标记器似乎失败了。

3）你能做的一件简单的事就是调用replace方法（尽管你可能需要使用re包来获得更复杂的模式），例如：

word_tokenized_no_punct_no_sw_no_apostrophe = [x.split("'") for x in word_tokenized_no_punct_no_sw]
word_tokenized_no_punct_no_sw_no_apostrophe = [y for x in word_tokenized_no_punct_no_sw_no_apostrophe for y in x]

它产生：

['ieri', 'andato', 'due', 'supermercati', 'oggi', 'volevo', 'andare', 'all', 'ippodromo', 'stasera', 'mangio', 'pizza', 'verdure']

2）模式的替代方案是treetagger，授予它不是最简单的安装（您需要python package和tool itself，但是在此部分之后它可以工作Windows和Linux）。

上面示例的一个简单示例：

import treetaggerwrapper 
from pprint import pprint

it_string = "Ieri sono andato in due supermercati. Oggi volevo andare all'ippodromo. Stasera mangio la pizza con le verdure."
tagger = treetaggerwrapper.TreeTagger(TAGLANG="it")
tags = tagger.tag_text(it_string)
pprint(treetaggerwrapper.make_tags(tags))

pprint收益：

[Tag(word=u'Ieri', pos=u'ADV', lemma=u'ieri'),
 Tag(word=u'sono', pos=u'VER:pres', lemma=u'essere'),
 Tag(word=u'andato', pos=u'VER:pper', lemma=u'andare'),
 Tag(word=u'in', pos=u'PRE', lemma=u'in'),
 Tag(word=u'due', pos=u'ADJ', lemma=u'due'),
 Tag(word=u'supermercati', pos=u'NOM', lemma=u'supermercato'),
 Tag(word=u'.', pos=u'SENT', lemma=u'.'),
 Tag(word=u'Oggi', pos=u'ADV', lemma=u'oggi'),
 Tag(word=u'volevo', pos=u'VER:impf', lemma=u'volere'),
 Tag(word=u'andare', pos=u'VER:infi', lemma=u'andare'),
 Tag(word=u"all'", pos=u'PRE:det', lemma=u'al'),
 Tag(word=u'ippodromo', pos=u'NOM', lemma=u'ippodromo'),
 Tag(word=u'.', pos=u'SENT', lemma=u'.'),
 Tag(word=u'Stasera', pos=u'ADV', lemma=u'stasera'),
 Tag(word=u'mangio', pos=u'VER:pres', lemma=u'mangiare'),
 Tag(word=u'la', pos=u'DET:def', lemma=u'il'),
 Tag(word=u'pizza', pos=u'NOM', lemma=u'pizza'),
 Tag(word=u'con', pos=u'PRE', lemma=u'con'),
 Tag(word=u'le', pos=u'DET:def', lemma=u'il'),
 Tag(word=u'verdure', pos=u'NOM', lemma=u'verdura'),
 Tag(word=u'.', pos=u'SENT', lemma=u'.')]

在lematizing之前，它还很好地标记了all'ippodromo到al和ippodromo（希望是正确的）。现在我们只需要应用删除停用词和标点符号就可以了。

用于python的

The doc for installing the TreeTaggerWrapper库