From the viewpoint of word alignment, the terminal unary rules provide the links of word pairs , whereas the binary rules represent the reordering factor.

For instance, there are two parses for three consecutive word pairs , viz.

The base step applies all relevant terminal unary rules to establish the links of word pairs .

The word pairs are then combined into span pairs in all possible ways.

HMM); Zhang and Gildea (2005) propose Tic-tac-toe pruning, which is based on the Model 1 probabilities of word pairs inside and outside a pair of spans.

The following features about alignment link are used in W-DITG: 1) Word pair translation probabilities trained from HMM model (Vogel, et.al., 1996) and IBM model 4 (Brown et.al., 1993; Och and Ney, 2000).

In the base step, only the word pairs listed in sentence-level annotation are inserted in the hypergraph, and the re-cursive steps are just the same as usual.

Zhang and Gildea (2005) show that Model 1 (Brown et al., 1993; Och and Ney., 2000) probabilities of the word pairs inside and outside a span pair ([ei1,ei2]/[jj-1,jj-2]) are useful.

probability of word pairs Within the span pair):

And we also propose an effective strategy for dependency projection, where the dependency relationships of the word pairs in the source language are projected to the word pairs of the target language, leading to a set of classification instances rather than a complete tree.

In this paper, we first describe an intuitionistic method for dependency parsing, which resorts to a classifier to determine whether a word pair forms a dependency edge, and then propose an effective strategy for dependency projection, which produces a set of projected classification instances rather than complete projected trees.

Given a word-aligned bilingual corpus with source language sentences parsed, the dependency relationships of the word pairs in the source language are projected to the word pairs of the target language.

A dependency relationship is a boolean value that represents whether this word pair forms a dependency edge.

The task of the word-pair classification model is to determine whether any candidate word pair , :10, and 553- st. 1 g i,j g |x| andz' 75 j, forms a dependency edge.

Ideally, given the classification results for all candidate word pairs , the dependency parse tree can be composed of the candidate edges with higher score (1 for the boolean-valued classifier, and large p for the real-valued classifier).

Here we give the calculation of dependency probability C (7', j We use w to denote the parameter vector of the ME model, and f (7', j, 7“) to denote the feature vector for the assumption that the word pair 7' and j has a dependency relationship 7“.

However, the goals of their work are different from ours in that their models mainly focus on mining cross-lingual topics of matching word pairs and discovering the correspondence at the vocabulary level.

Therefore, the topics extracted using their model cannot indicate how a common topic is covered diflerently in the two languages, because the words in each word pair share the same probability in a common topic.

In our model, since we only add a soft constraint on word pairs in the dictionary, their probabilities in common topics are generally different, naturally capturing which shows the different variations of a common topic in different languages.

Thus when a cross-lingual topic picks up words that co-occur in monolingual text, it would prefer picking up word pairs whose translations in other languages also co-occur with each other, giving us a coherent multilingual word distribution that characterizes well the content of text in different languages.

Figure 1 shows an example of the potentially collocated word pairs aligned by the MWA method.

Then the probability for each aligned word pair is estimated as follows:

word pair calculated according to Eq.

CL-SCL comprises three steps: In the first step, CL-SCL selects word pairs {2123,2127}, called pivots, where 7.03 E V3 and 2127 6 V7.

Considering our sentiment classification example, the word pair {excellent3, exzellentT} satisfies both conditions: (1) the words are strong indicators of positive sentiment,

Second, for each word 1115 6 VP we find its translation in the target vocabulary V7 by querying the translation oracle; we refer to the resulting set of word pairs as the candidate pivots, P’ :