We create binary indicator features for each model using feature templates .

Our feature template definitions build from those used by the top performing systems in the CoNLL—2009 Shared Task, Zhao et al.

Template Creation Feature templates are defined over triples of (property, positions, order).

4.2 Feature Template Sets

Further, the best-performing low-resource features found in this work are those based on coarse feature templates and selected by information gain.

#FT indicates the number of feature templates used (unigrams+bigrams).

(2009) features, who use feature templates from combinations of word properties, syntactic positions including head and children, and semantic properties; and features from Bj orkelund et a1.

We build up a small set of feature templates as part of a discriminative constituency parser and outperform the Berkeley parser on a wide range of languages.

Subsequent lines in Table 1 indicate additional surface feature templates computed over the span, which are then conjoined with the rule identity as shown in Figure l to give additional features.

Note that many of these features have been used before (Taskar et al., 2004; Finkel et al., 2008; Petrov and Klein, 2008b); our goal here is not to amass as many feature templates as possible, but rather to examine the extent to which a simple set of features can replace a complicated state space.

Because heads of constituents are often at the beginning or the end of a span, these feature templates can (noisily) capture monolexical properties of heads without having to incur the inferential cost of lexicalized annotations.

We exploit this by adding an additional feature template similar to our span shape feature from Section 4.4 which uses the (deterministic) tag for each word as its descriptor.

To improve the performance of our X-bar grammar, we will add a number of surface feature templates derived only from the words in the sentence.

Features For the arc feature vector gbhflm, we use the same set of feature templates as MST V0.5 .1.

For head/modifier vector gbh and gbm, we show the complete set of feature templates used by our model in Table 1.

Finally, we use a similar set of feature templates as Turbo V2.1 for 3rd order parsing.

A predominant way to counter the high dimensionality of features is to manually design or select a meaningful set of feature templates , which are used to generate different types of features (McDonald et al., 2005a; Koo and Collins, 2010; Martins et al., 2013).

Table 1: Word feature templates used by our model.

Table 1: PCS tag feature templates .

First- to Third-Order Features The feature templates of first— to third-order features are mainly drawn from previous work on graph-based parsing (McDonald and Pereira, 2006), transition-based parsing (Nivre et al., 2006) and dual decomposition-based parsing (Martins et al., 2011).

The feature templates are inspired by previous feature-rich POS tagging work (Toutanova et al., 2003).

In our work we use feature templates up to 5-gram.

However, some feature templates in Table 1 become unavailable, because POS tags for the lookahead words are not specified yet under the joint framework.

However, all the feature templates given in Table l are just some simple structural features.

Type Feature Templates

Table 1 lists the feature templates used in our baseline parser, which is adopted from Zhang and Clark (2009).

Table 1: Feature templates encoding intra-word dependencies.

It adjusts the weights of segmentation and POS-tagging features, because the number of feature templates is much less for the two tasks than for parsing.