Clone of "A Good Part-of-Speech Tagger in about 200 Lines of Python" by Matthew Honnibal
Home Page: http://www.hankcs.com/nlp/averaged-perceptron-tagger.html
License: MIT License
您好，请问200行Python代码实现感知机词性标注器 中求平均权重的思路与这篇文章第10页中返回平均权重的思路有何不同呢？我现在比较困惑，烦请您解答一下^_^
你好，我想请教一下200行Python代码实现感知机词性标注器中【搜索】哪一节中train函数的一个问题：
文中前端说的是，当guess != true_tag时就更新权重（犯错时），第一个train出现的时候。可是【搜索】这一段中却总是在更新，是遗漏了吗？
guess = self.tagdict.get(word)
if not guess:
feats = self._get_features(
i, word, context, prev, prev2)
guess = self.model.predict(feats)
self.model.update(tags[i], guess, feats)
# Set the history features from the guesses, not the
# true tags
AveragedPerceptron.py的96~97行，model.predict(features)中返回的是guess_tag，而不是每个tag对应的评分：
scores = model.predict(features)
guess, score = max(scores.items(), key=lambda i: i[1])
if guess != class_:
model.update(class_, guess, features)
是不是应该这样：
guess = model.predict(features)
if guess != class_:
model.update(class_, guess, features)
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