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Sequence labeling

本文是阅读维基百科Sequence labeling的笔记。

In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values.

显然，sequence labeling所处理的是sequence结构的数据，显然，它的输出也是一个sequence。

生活中哪些数据是具备sequence结构的呢？

Sequence labeling can be treated as a set of independent classification tasks, one per member of the sequence. However, accuracy is generally improved by making the optimal label for a given element dependent on the choices of nearby elements, using special algorithms to choose the globally best set of labels for the entire sequence at once.

这段话其实描述了解决sequence label问题的两种思路：

  • independent，即“labeling one item at a time”
  • dependent，即“finding the globally best label sequence”

原文的下一段结合part-of-speech tagging对这两种方式进行了比较。

Most sequence labeling algorithms are probabilistic in nature, relying on statistical inference to find the best sequence. The most common statistical models in use for sequence labeling make a Markov assumption, i.e. that the choice of label for a particular word is directly dependent only on the immediately adjacent labels; hence the set of labels forms a Markov chain. This leads naturally to the hidden Markov model (HMM), one of the most common statistical models used for sequence labeling. Other common models in use are the maximum entropy Markov model and conditional random field.

上面这段话中的**statistical models**参见Statistical model，**Markov assumption**参见Markov property。