Lemmatizing Italian sentences for frequency counting

Question

I would like to lemmatize some Italian text in order to perform some frequency counting of words and further investigations on the output of this lemmatized content.

I am preferring lemmatizing than stemming because I could extract the word meaning from the context in the sentence (e.g. distinguish between a verb and a noun) and obtain words that exist in the language, rather than roots of those words that don't usually have a meaning.

I found out this library called pattern (pip2 install pattern) that should complement nltk in order to perform lemmatization of the Italian language, however I am not sure the approach below is correct because each word is lemmatized by itself, not in the context of a sentence.

Probably I should give pattern the responsibility to tokenize a sentence (so also annotating each word with the metadata regarding verbs/nouns/adjectives etc), then retrieving the lemmatized word, but I am not able to do this and I am not even sure it is possible at the moment?

Also: in Italian some articles are rendered with an apostrophe so for example "l'appartamento" (in English "the flat") is actually 2 words: "lo" and "appartamento". Right now I am not able to find a way to split these 2 words with a combination of nltk and pattern so then I am not able to count the frequency of the words in the correct way.

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]
    )
)

Gives this output:

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)

How to effectively lemmatize some sentences with pattern using their tokenizer? (assuming lemmas are recognized as nouns/verbs/adjectives etc.)
Is there a python alternative to pattern to use for Italian lemmatization with nltk?
How to split articles that are bound to the next word using apostrophes?

Lemmatizing Italian sentences for frequency counting

1 Answers