We extract dependency paths for each pair of named entities in one sentence.

for words on the dependency paths .

Each entity pair tun and the dependency path which connects them form wit a tuple.

Such patterns could be sequences of lemmas and Part-of-Speech tags, or lexicalized dependency paths .

Whether we use sequences or dependency paths , we will encounter the problem of polysemy.

We perform experiments on New York Times articles and consider lexicalized dependency paths as patterns in our data.

Both DIRT and our approach represent dependency paths using their arguments.

Two ways of extracting patterns have been used: (a) Syntactic, taking the dependency path between the two entities, and (b) Intertext, taking the text between the two.

This context may be a complex structure, such as the dependency path joining the two entities, but it is considered for our purposes as a single term; (e) for each relation r relating 67; with 63-, document Dij is added to collection CT.

The words in each document can be, for example, all the dependency paths that have been observed in the input textual corpus between the two related entities.

Generative model Once these collections are built, we use the generative model from Figure 2 to learn the probability that a dependency path is conveying some relation between the entities it connects.