For the analysis of word pair features, we use a large collection of automatically extracted explicit examples from the experiments in Blair-Goldensohn et al.

For the complete set of 10,000 examples, word pair features were computed.

After removing word pairs that appear less than 5 times, the remaining features were ranked by information gain using the MALLET toolkitl.

We examine the most informative word pair features and find that they are not the semantically-related pairs that researchers had hoped.

Indeed, word pairs form the basic feature of most previous work on classifying implicit relations (Marcu and Echihabi, 2001; Blair-Goldensohn et al., 2007; Sporleder and Lascarides, 2008) or the simpler task of predicting which connective should be used to express a relation (Lapata and Lascarides, 2004).

Semantic relations vs. function word pairs If the hypothesis for word pair triggers of discourse relations were true, the analysis of unambiguous relations can be used to discover pairs of words with causal or contrastive relations holding between them.

At the same time, feature selection is always necessary for word pairs , which are numerous and lead to data sparsity problems.

Using an ensemble method, the key information extracted from word pairs with dependency relations in the translated text is effectively integrated into the parser for the target language.

In each step, the classifier checks a word pair , namely, 5, the top of a stack that consists of the processed words, and, i, the first word in the (input) unprocessed sequence, to determine if a dependent relation should be established between them.

As we cannot expect too much for a word-by-word translation, only word pairs with dependency relation in translated text are extracted as useful and reliable information.

Then some features based on a query in these word pairs according to the current parsing state (namely, words in the current stack and input) will be derived to enhance the Chinese parser.

As all concerned feature values here are calculated from the searching result in the translated word pair list according to the current parsing state, and a complete and exact match cannot be always expected, our solution to the above segmentation issue is using a partial matching strategy based on characters that the words include.

However, dependency parsing focuses on the relations of word pairs , this allows us to use a dictionary-based translation without assuming a parallel corpus available, and the training stage of translation may be ignored and the decoding will be quite fast in this case.

We have also provided the first intrinsic evaluation of word translation probabilities with respect to human relatedness rankings for reference word pairs .

To do so, we compare translation probabilities with concept vector based semantic relatedness measures with respect to human relatedness rankings for reference word pairs .

In this study, we use the correlation with human rankings for reference word pairs to investigate how word translation probabilities compare with traditional semantic relatedness measures.

The aim of this first experiment is to perform an intrinsic evaluation of the word translation probabilities obtained by comparing them to traditional semantic relatedness measures on the task of ranking word pairs .

This dataset comprises two subsets, which have been annotated by different annotators: Fin1—153, containing 153 word pairs, and Fin2—200, containing 200 word pairs .

In order to ensure a fair evaluation, we limit the comparison to the word pairs which are contained in all resources and translation tables.

which is defined as the word translation probability of the aligned word pair divided by the sum of the translation probabilities over all the target words in the sentence.

Intuitively, the above link confidence definition compares the lexical translation probability of the aligned word pair with the translation probabilities of all the target words given the source word.

On the other hand, additional information (such as the distance of the word pair , the alignment of neighbor words) could indicate higher likelihood for the alignment link.

Furthermore, it is possible to create new links by relinking unaligned source and target word pairs within the context window if their context-dependent link posterior probability is high.

It is the product of lexical translation probabilities for the aligned word pairs .