In order to make the formalism general enough to include these parsers, we define items in terms of sets of partial dependency trees as shown in Figure 1.

Such spans cannot be represented by a single dependency tree .

Therefore, our formalism allows items to be sets of forests of partial dependency trees , instead of sets of trees.

Figure l: The dependency tree for sentence the boy will find it interesting

1.2 Dependency Trees

Dependency trees reveal long-distance relations between words.

In one kind, we keep dependency trees with a sub-root, where all the children of the sub-root are complete.

Figure 5: A dependency tree with flexible combination

Figure 1 shows a traditional dependency tree .

Given a sentence X = (x1, ..., sun) (cc,- denotes each word in the sentence), we are interested in computing a directed dependency tree , Y, over X.

We assume that a directed dependency tree Y consists of ordered pairs (sci —> any) of words in X such that each word appears in at least one pair and each word has in-degree at most one.

Dependency trees are assumed to be projective here, which means that if there is an arc (cc,- —> 553-), then :0,- is an ancestor of all the words

For experiment on English, we used the English Penn Treebank (PTB) (Marcus et al., 1993) and the constituency structures were converted to dependency trees using the same rules as (Yamada and Matsumoto, 2003).

Figure l: A dependency tree

As mentioned in Section 3, a dependency tree Yj is represented as an adjacency matrix.

Thus we need to enforce some constraints in the adjacency matrix to make sure that each Yj satisfies the dependency tree constraints.

1We assume all the dependency trees are projective in our work (just as some other researchers do), although in the real word, most languages are non-projective.

We represent a dependency tree as a k x k adjacency matrix.

We add syntax to this process with a cohesion constraint based on a dependency tree for the source sentence.

The decoder stores the flat sentence in the original sentence data structure, and the head-encoded dependency tree in an attached tree data structure.

Next, we introduce our source dependency tree T. Each source token e,- is also a node in T. We define T(ei) to be the subtree of T rooted at 61-.

spanS 6-, T, am 2 min a -, max ak, ( z 1 > {j|€j€T(€i)} j {k|€k€T(€i)} Consider the simple phrasal translation shown in Figure 1 along with a dependency tree for the English source.

This algorithm was used to implement a soft cohesion constraint for the Moses decoder, based on a source-side dependency tree .

Since we require source dependency trees , all experiments test English to French translation.

English dependency trees are provided by Minipar (Lin, 1994).

For the protein pair IL-8 and CXCR1 in Figure 4, a dependency parser outputs a dependency tree shown in Figure 1.

From this dependency tree , we can extract a dependency path shown in Figure 5, which appears to be a strong clue in knowing that these proteins are mentioned as interacting.

CoNLL The dependency tree format used in the 2006 and 2007 CoNLL shared tasks on dependency parsing.

Figure 1 shows a dependency tree for the sentence “IL-8 recognizes and activates CXCRl.” An advantage of dependency parsing is that dependency trees are a reasonable approximation of the semantics of sentences, and are readily usable in NLP applications.

Figure l: CoNLL-X dependency tree

The analytical layer roughly corresponds to the surface syntax of the sentence; the annotation is a single-rooted dependency tree with labeled nodes.

Again, the annotation is a dependency tree with labeled nodes (Hajicova 1998).

The representation of the tectogrammatical annotation of a sentence is a rooted dependency tree .