Moreover, when deterministic constraints have applied to contextual words of wo, it is also possible to include some lookahead feature templates , such as:

Character-based feature templates

We adopt the ’non—leXical-target’ feature templates in (Jiang et al., 2008a).

The word-based feature templates derived for the label y, are as follows:

For each label y, we use the feature templates as follows:

The latter two feature templates are designed to detect character or word reduplication, a morphological phenomenon that can influence word segmentation in Chinese.

Table 1: DLM-based feature templates

3.3 DLM-based feature templates

The feature templates are outlined in Table l, where TYPE refers to one of the typeszPL or PR, h_pos refers to the part-of-speech tag of :1: h, h_word refers to the lexical form of :1: h, ch_pos refers to the part-of-speech tag of mch, and ch_word refers to the lexical form of mm.

In order to make these models comparable, we use two comparable sets of feature templates : one adapted to sequence labelling (CRF—based MWER) and the other one adapted to reranking (MaXEnt-based reranker).

All feature templates are given in table 2.

Table 2: Feature templates (f) used both in the MWER and the reranker models: n is the current position in the sentence, is the word at position i; is the part-of-speech tag of w(z’); if the word at absolute position i is part of a compound in the Shortest Path Segmentation, mwt(i) and mws(i) are respectively the part-of-speech tag and the internal structure of the compound, mwpos(i) indicates its relative position in the compound (B or I).

We need to redesign our ranking feature templates to encode the reordering information in the source part of the translation rules.

The detailed feature templates are shown in Table l.

Table 1: Feature templates for ranking function.