were the ones that had been counted”, using the feature templates in Table l, at least four times for all of the (i, j) position pairs in the training sentences.

We conjoined the features with three types of label pairs (C, I), (LN), or (C,N> as instances of the feature template (1,, [3-) to produce features for SEQUENCE.

We used the following feature templates to produce features for the outbound model: <8i—2>a <8i—1>a <80, <8i+1>a <8i+2>a <72), 02—1772), (25,-, n+1), and (3,, ti).

Table 1: Feature templates .

Table 1 shows the feature templates used to produce the features.

A feature is an instance of a feature template .

In this section, we first introduce how different types of feature templates are designed, and then show an example of how the features help transfer the syntactic structure information.

Note that the same feature templates are used for all the target grammar formalisms.

We define the following feature templates : fbmary for binary derivations, funary for unary derivations, and from; for the root nodes.

the baseline feature templates of joint S&T are the ones used in (Ng and Low, 2004; Jiang et al., 2008), as shown in Table l. A = {A1A2...AK} E RK are the weight parameters to be learned.

Table l: The feature templates of joint S&T.

The feature templates are from Zhao et al.

The same feature templates in (Wang et al., 2011) are used, i.e., "+n-gram+cluster+leXicon".

The feature templates introduced in Section 3.1 are used.

But overall, our approach differs in three important aspects: first, novel feature templates are defined for measuring the similarity between vertices.

For the arc-eager parser, we use the feature template of Zhang and Nivre (2011).

It turns out that our feature template , described in §4.3, is the exact merge of the templates used for the arc-eager and the arc-standard parsers.

These features are then combined together into complex features, according to some feature template , and joined with the available transition types.

Our feature template is an extended version of the feature template of Zhang and Nivre (2011), originally developed for the arc-eager model.

The sparse feature templates resulted here in a total of 4.9 million possible features, of which again only a fraction were active, as shown in Table 2.

Table 2: Active sparse feature templates

These feature templates resulted in a total of 3.4 million possible features, of which only a fraction were active for the respective tuning set and optimizer, as shown in Table 2.

Table 3 is the feature template we set initially which generates 722 999 637 features.

Table 3: feature templates for CRFs training

We used 5-fold cross-validation performed using the training data to tweak the included feature templates and optimize training parameters.

The following feature templates are used to generate features from the above words.

To evaluate the importance of the different types of features, the same experiment was rerun multiple times, each time including or excluding exactly one feature template .

Table 1 shows the feature templates of our model.

The feature templates in bold are novel, are designed to encode head character information.

We find that the parsing accuracy decreases about 0.6% when the head character related features (the bold feature templates in Table l) are removed, which demonstrates the usefulness of these features.