In addition, there is often a close connection between the meaning of a Chinese word and its first or last character.

Chinese word should be strictly segmented according to the guideline before POS tags and dependency relations are annotated.

English treebank is translated into Chinese word by word, Chinese words in the translated text are exactly some entries from the bilingual lexicon, they are actually irregular phrases, short sentences or something else rather than words that follows any existing word segmentation convention.

Translate the PTB text into Chinese word by word.

After the target sentence is generated, the attached POS tags and dependency information of each English word will also be transferred to each corresponding Chinese word .

Although we try to perform an exact word-by-word translation, this aim cannot be fully reached in fact, as the following case is frequently encountered, multiple English words have to be translated into one Chinese word .

We test the efficacy of this method in the context of Chinese word segmentation and part-of-speech tagging, where no segmentation and POS tagging standards are widely accepted due to the lack of morphology in Chinese.

To test the efficacy of our method we choose Chinese word segmentation and part-of-speech tagging, where the problem of incompatible annotation standards is one of the most evident: so far no segmentation standard is widely accepted due to the lack of a clear definition of Chinese words , and the (almost complete) lack of morphology results in much bigger ambiguities and heavy debates in tagging philosophies for Chinese parts-of-speech.

where each subsequence Cm- indicates a Chinese word spanning from characters 0,- to Cj (both in-

Xue and Shen (2003) describe for the first time the character classification approach for Chinese word segmentation, Where each character is given a boundary tag denoting its relative position in a word.

It is an online training algorithm and has been successfully used in many NLP tasks, such as POS tagging (Collins, 2002), parsing (Collins and Roark, 2004), Chinese word segmentation (Zhang and Clark, 2007; J iang et al., 2008), and so on.

In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging.

In this paper, we presented a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging.

Previous studies on joint Chinese word segmentation and POS tagging have used Penn Chinese Treebank (CTB) (Xia et al., 2000) in experiments.

For example, a perceptron algorithm is used for joint Chinese word segmentation and POS tagging (Zhang and Clark, 2008; Jiang et al., 2008a; Jiang et al., 2008b).

Since parsing of the source is relatively inexpensive compared to the target side, it would be relatively easy to condition head-modifier dependencies not only on the two target words, but also on their corresponding Chinese words and their relative positions in the Chinese tree.

Chinese words drawn from various news parallel corpora distributed by the Linguistic Data Consortium (LDC).

Chinese words were automatically segmented with a conditional random field (CRF) classifier (Chang et al., 2008) that conforms to the Chinese Treebank (CTB) standard.

A Chinese NE C0={CW1, CW2, ..., CWn} is a sequence of Chinese words CW, and the English

The selection of the Chinese words to be translated will take into consideration both the translation confidence of the words and the information contents that they contain for the whole ON.

When Chinese words are aligned with English words, the mistakes made in Chinese segmentation may result in wrong alignment results.