def default_tag():
#Tagging any word by assigning the most frequent tag in a given corpus
tags = []
for (word, tag) in brown.tagged_words(categories='news'):
tags.append(tag)
most_freq_tag = FreqDist(tags).max()
raw = 'I love AIT because AIT is interesting and professors here give a lot of challenging assignment'
tokens = word_tokenize(raw)
#Here is our tagger, it means in default, it will assign 'NN' tag to a word input
default_tagger = DefaultTagger('NN')
tagged = default_tagger.tag(tokens)
print(tagged)
score = default_tagger.evaluate(brown_tagged_sents)
print(score)
#getting the most common tag in the brown corpus tags = [tag for (word, tag) in brown.tagged_words()] most_common_tag = nltk.FreqDist(tags).max() print(most_common_tag) from nltk import DefaultTagger barack = """Barack Hussein Obama (born August 4, 1961) is an American politician who served as the 44th President of the United States from January 20, 2009, to January 20, 2017. A member of the Democratic Party, he was the first African American to assume the presidency and previously served as a United States Senator from Illinois (2005–2008).""" tokenized_barack = word_tokenize(barack) default_tagger = DefaultTagger(most_common_tag) def_tagged_barack = default_tagger.tag(tokenized_barack) print(def_tagged_barack) #Lookup Tagger #Ngram tagger message = "the quick brown fox jumped over the lazy dog" training_tag = pos_tag(word_tokenize(message)) print(training_tag) #training the ngram tagger ngram_tagger = nltk.NgramTagger(n=2, train=[training_tag]) message2 = "the lazy dog jumped over the quick brown fox" message2_tags = ngram_tagger.tag(word_tokenize(message2)) print(message2_tags) print(list(nltk.ngrams(pos_tag(word_tokenize(message)), n=2)))
import nltk
nltk.download('brown')
from nltk.corpus import brown
brown_news_tagged = brown.tagged_sents(categories='news', tagset='universal')
brown_news_words = brown.tagged_words(categories='news', tagset='universal')
brown_train = brown_news_tagged[100:]
brown_test = brown_news_tagged[:100]
from nltk.tag import untag
test_sent = untag(brown_test[0])
print("Tagged: ", brown_test[0])
print()
print("Untagged: ", test_sent)
# A default tagger assigns the same tag to all words
from nltk import DefaultTagger
default_tagger = DefaultTagger('NOUN')
default_tagger.tag('This is a test'.split())
nltk.help.upenn_tagset('VB.*')
text = nltk.word_tokenize("I cannot bear the pain of bear")
out = nltk.pos_tag(text)
out = nltk.tag.str2tuple('bear/NN')
print(out)
print((out[0], out[1]))
print(nltk.tag.tuple2str(out))
treebank_tagged = treebank.tagged_words(tagset='universal')
tag = nltk.FreqDist(tag for (word, tag) in treebank_tagged)
out = tag.most_common()
print(out)
tag = DefaultTagger('NN')
out = tag.tag(['Beautiful', 'morning'])
print(out)
"""英语的十大词类
1.名词noun n.
2.代词pronoun pron.
3.形容词adjective adj.
4.副词 adverb adv.
5.动词verb v.
6.数词numeral num.
7.冠词article art.
8.介词preposition prep.
9.连词conjunction conj.
10.感叹词interjection interj.
"""
print("**********************************")
out = nltk.corpus.words.fileids()