gensim - gensim LabeledSentence と TaggedDocument の違いは何ですか

Question

TaggedDocumenthowとLabeledSentenceof gensimworksの違いを理解するのを手伝ってください。私の最終的な目標は、Doc2Vecモデルと任意の分類子を使用したテキスト分類です。私はこのブログをフォローしています！

class MyLabeledSentences(object):
    def __init__(self, dirname, dataDct={}, sentList=[]):
        self.dirname = dirname
        self.dataDct = {}
        self.sentList = []
    def ToArray(self):       
        for fname in os.listdir(self.dirname):            
            with open(os.path.join(self.dirname, fname)) as fin:
                for item_no, sentence in enumerate(fin):
                    self.sentList.append(LabeledSentence([w for w in sentence.lower().split() if w in stopwords.words('english')], [fname.split('.')[0].strip() + '_%s' % item_no]))
        return sentList


class MyTaggedDocument(object):
    def __init__(self, dirname, dataDct={}, sentList=[]):
        self.dirname = dirname
        self.dataDct = {}
        self.sentList = []
    def ToArray(self):       
        for fname in os.listdir(self.dirname):            
            with open(os.path.join(self.dirname, fname)) as fin:
                for item_no, sentence in enumerate(fin):
                    self.sentList.append(TaggedDocument([w for w in sentence.lower().split() if w in stopwords.words('english')], [fname.split('.')[0].strip() + '_%s' % item_no]))
        return sentList

sentences = MyLabeledSentences(some_dir_name)
model_l = Doc2Vec(min_count=1, window=10, size=300, sample=1e-4, negative=5,     workers=7)
sentences_l = sentences.ToArray()
model_l.build_vocab(sentences_l )
for epoch in range(15): # 
    random.shuffle(sentences_l )
    model.train(sentences_l )
    model.alpha -= 0.002  # decrease the learning rate
    model.min_alpha = model_l.alpha 

sentences = MyTaggedDocument(some_dir_name)
model_t = Doc2Vec(min_count=1, window=10, size=300, sample=1e-4, negative=5, workers=7)
sentences_t = sentences.ToArray()
model_l.build_vocab(sentences_t)
for epoch in range(15): # 
    random.shuffle(sentences_t)
    model.train(sentences_t)
    model.alpha -= 0.002  # decrease the learning rate
    model.min_alpha = model_l.alpha

私の質問model_l.docvecs['some_word']はmodel_t.docvecs['some_word']? TaggedDocumentどのように動作するかを把握するための優れた情報源のウェブリンクを提供していただけますかLabeledSentence。