问NLTK使用语料库标注西班牙语单词

EN

from nltk.corpus import cess_esp as cess
from nltk import UnigramTagger as ut
from nltk import BigramTagger as bt

# Read the corpus into a list, 
# each entry in the list is one sentence.
cess_sents = cess.tagged_sents()

# Train the unigram tagger
uni_tag = ut(cess_sents)

sentence = "Hola , esta foo bar ."

# Tagger reads a list of tokens.
uni_tag.tag(sentence.split(" "))

# Split corpus into training and testing set.
train = int(len(cess_sents)*90/100) # 90%

# Train a bigram tagger with only training data.
bi_tag = bt(cess_sents[:train])

# Evaluates on testing data remaining 10%
bi_tag.evaluate(cess_sents[train+1:])

# Using the tagger.
bi_tag.tag(sentence.split(" "))

#-*- coding: utf8 -*-

from nltk import UnigramTagger as ut
from nltk import BigramTagger as bt
from cPickle import dump,load

def loadtagger(taggerfilename):
    infile = open(taggerfilename,'rb')
    tagger = load(infile); infile.close()
    return tagger

def traintag(corpusname, corpus):
    # Function to save tagger.
    def savetagger(tagfilename,tagger):
        outfile = open(tagfilename, 'wb')
        dump(tagger,outfile,-1); outfile.close()
        return
    # Training UnigramTagger.
    uni_tag = ut(corpus)
    savetagger(corpusname+'_unigram.tagger',uni_tag)
    # Training BigramTagger.
    bi_tag = bt(corpus)
    savetagger(corpusname+'_bigram.tagger',bi_tag)
    print "Tagger trained with",corpusname,"using" +\
                "UnigramTagger and BigramTagger."
    return

# Function to unchunk corpus.
def unchunk(corpus):
    nomwe_corpus = []
    for i in corpus:
        nomwe = " ".join([j[0].replace("_"," ") for j in i])
        nomwe_corpus.append(nomwe.split())
    return nomwe_corpus

class cesstag():
    def __init__(self,mwe=True):
        self.mwe = mwe
        # Train tagger if it's used for the first time.
        try:
            loadtagger('cess_unigram.tagger').tag(['estoy'])
            loadtagger('cess_bigram.tagger').tag(['estoy'])
        except IOError:
            print "*** First-time use of cess tagger ***"
            print "Training tagger ..."
            from nltk.corpus import cess_esp as cess
            cess_sents = cess.tagged_sents()
            traintag('cess',cess_sents)
            # Trains the tagger with no MWE.
            cess_nomwe = unchunk(cess.tagged_sents())
            tagged_cess_nomwe = batch_pos_tag(cess_nomwe)
            traintag('cess_nomwe',tagged_cess_nomwe)
            print
        # Load tagger.
        if self.mwe == True:
            self.uni = loadtagger('cess_unigram.tagger')
            self.bi = loadtagger('cess_bigram.tagger')
        elif self.mwe == False:
            self.uni = loadtagger('cess_nomwe_unigram.tagger')
            self.bi = loadtagger('cess_nomwe_bigram.tagger')

def pos_tag(tokens, mmwe=True):
    tagger = cesstag(mmwe)
    return tagger.uni.tag(tokens)

def batch_pos_tag(sentences, mmwe=True):
    tagger = cesstag(mmwe)
    return tagger.uni.batch_tag(sentences)

tagger = cesstag()
print tagger.uni.tag('Mi colega me ayuda a programar cosas .'.split())

import es_core_news_sm
nlp = es_core_news_sm.load()
doc = nlp("El copal se usa principalmente para sahumar en distintas ocasiones como lo son las fiestas religiosas.")
print([(w.text, w.pos_) for w in doc])

displacy.render(doc, style='dep', jupyter = True, options = {'distance': 120})