※ 引述《yuseke (yuseke)》之銘言： : as title : 這兩天在看RE的部分 : 根據PYTHON 基金會的網站: : https://docs.python.org/2/library/re.html : 7.2.5.7. Finding all Adverbs and their Positions¶ : If one wants more information about all matches of a pattern than the matched : text, finditer() is useful as it provides instances of MatchObject instead of : strings. Continuing with the previous example, if one was a writer who wanted : to find all of the adverbs and their positions in some text, he or she would : use finditer() in the following manner: : ... print '%02d-%02d: %s' % (m.start(), m.end(), m.group(0)) : 07-16: carefully : 40-47: quickly : 關於""""for m in re.finditer(r"\w+ly", text):"""" : 這個部份我有一個疑問, : 可是並不是所有的adv都有ly字尾....... : 這種情形該怎麼處理呢? 在自然語言處理中，一個常用的方法是建機率模型 就是統計連續二個或三個POS出現的組合 假設訓練資料中，(AT, NN)後面出現的機率最高是IN 則遇到(AT,NN,??)的時候，就填NN 一般會用trigram tagger，以三個POS為主 如果test data出現沒見過的POS組合，就查bigram tagger 如果再查不到，就一律視為NN 下面是用brown corpus作訓練資料，作一個簡單的tagger 也可以參考下面的網頁 http://www.nltk.org/book/ch05.html import nltk train = nltk.corpus.brown.tagged_sents() train[0] # [(u'The', u'AT'), # (u'Fulton', u'NP-TL'), # (u'County', u'NN-TL'), # (u'Grand', u'JJ-TL'), # (u'Jury', u'NN-TL'), # (u'said', u'VBD'), # (u'Friday', u'NR'), # (u'an', u'AT'), # (u'investigation', u'NN'), # (u'of', u'IN'), # (u"Atlanta's", u'NP$'), # (u'recent', u'JJ'), # (u'primary', u'NN'), # (u'election', u'NN'), # (u'produced', u'VBD'), # (u'``', u'``'), # (u'no', u'AT'), # (u'evidence', u'NN'), # (u"''", u"''"), # (u'that', u'CS'), # (u'any', u'DTI'), # (u'irregularities', u'NNS'), # (u'took', u'VBD'), # (u'place', u'NN'), # (u'.', u'.')] # backoff是遇到沒遇過的組合時，要作的動作 default_tagger = nltk.DefaultTagger('NN') bi_tagger = nltk.BigramTagger(train, backoff=default_tagger) tri_tagger = nltk.TrigramTagger(train, backoff=bi_tagger) # 用訓練出的tagger測試 tri_tagger.tag('John is a dog'.split()) # [('John', u'NP'), ('is', u'BEZ'), ('a', u'AT'), ('dog', 'NN')] --

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