- (Wallach, 2004) ⇒ Hanna M. Wallach. (2004). “Conditional Random Fields: An introduction." Technical Report MS-CIS-04-21, Department of Computer and Information Science, University of Pennsylvania.
Subject Headings: Linear-Chain Conditional Random Field
1 Labeling Sequential Data
The task of assigning label sequences to a set of observation sequences arises in many fields, including bioinformatics, computational linguistics and speech recognition [6, 9, 12] …
2 Undirected Graphical Models
A conditional random field may be viewed as an undirected graphical model, or Markov random field , globally conditioned on X, the random variable representing observation sequences. Formally, we define G = (V,E) to be an undirected graph such that there is a node [math]v \in V[/math] corresponding to each of the random variables representing an element Yv of Y . If each random variable Yv obeys the Markov property with respect to G, then (Y, X) is a conditional random field. In theory the structure of graph G may be arbitrary, provided it represents the conditional independencies in the label sequences being modeled. However, when modeling sequences, the simplest and most common graph structure encountered is that in which the nodes corresponding to elements of Y form a simple first-order chain, as illustrated in Figure 1.
|2004 ConditionalRandomFieldsAnIntro||Hanna M. Wallach||Conditional Random Fields: An introduction||http://www.cs.umass.edu/~wallach/technical reports/wallach04conditional.pdf||2004|
|Author||Hanna M. Wallach +|
|title||Conditional Random Fields: An introduction +|
|titleUrl||http://www.cs.umass.edu/~wallach/technical reports/wallach04conditional.pdf +|