   Next: Data and Evaluation Up: Overview of the Algorithm Previous: The Phrase Break Model

## Combining the Models

A network of TN-1 nodes and TN arcs is constructed (N=1 is a special case and has the same topology as N=2 - see figure 1) . Each node represents a juncture type, and when N>2 the nodes represent a juncture in the context of previous junctures. The POS sequence probabilities do not take account of context, and so for a given juncture type are the same no matter where the node occurs in the network. For example, if N=3, we will have 2 break nodes, one for when the previous juncture was a break and one for when the previous juncture was a non-break. These nodes have the same observation probabilities. Figure 1 shows networks for N=1, N=2 and N=3. Under this formulation we have the likelihood P(Ci|ji) (the POS sequence model) representing the relationship between tags and juncture types, and P(ji | ji-1, ..., ji-N+1) (the n-gram phrase break model) which represents the a priori probability of a sequence of juncture types occurring. This is used to give a basic regularity to the phrase break placement, enforcing the notion that phrase breaks are not simply a consequence of local word information.

The probability we are interested in is P(ji) given the previous sequence of junctures and the POS sequence at that point. This probability can be rewritten as follows:

 P(ji|Ci, JNi-1) = P((ji| JNi-1)| Ci) (4)

and using Bayes equation (5)

We make the assumption that the probabilities of all states of a particular juncture type are equal (e.g. P(Ci | break, non-break) = P(Ci | break, break)), so

 P(Ci | (ji | JNi-1)) = P(Ci | ji) (6)

and from equation 5, the probability of a juncture type given the preceding types and POS sequence becomes (7)   Next: Data and Evaluation Up: Overview of the Algorithm Previous: The Phrase Break Model
Alan W Black
1999-03-20