Monolingual translation probabilities have recently been introduced in retrieval models to solve the lexical gap problem.

We also show that the monolingual translation probabilities obtained (i) are comparable to traditional semantic relatedness measures and (ii) significantly improve the results over the query likelihood and the vector-space model for answer finding.

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

Section 3 presents the monolingual parallel datasets we used for obtaining monolingual translation probabilities .

We used the GIZA++ SMT Toolkit4 (Och and Ney, 2003) in order to obtain word-to-word translation probabilities from the parallel datasets described above.

The first method consists in a linear combination of the word-to-word translation probabilities after training:

The main drawback lies in the availability of suitable training data for the translation probabilities .

The rationale behind translation-based retrieval models is that monolingual translation probabilities encode some form of semantic knowledge.

While classical measures of semantic relatedness have been extensively studied and compared, based on comparisons with human relatedness judgements or word-choice problems, there is no comparable intrinsic study of the relatedness measures obtained through word translation probabilities .

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.

2 82“ ' > 2.1/mans) ( ) When computing the source-to-target alignment posterior probability, the numerator is the sentence translation probability calculated according to the given alignment A:

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

The denominator is the sentence translation probability summing over all possible alignments, which can be calculated similar to IBM Model 1 in (Brown et al., 1994)

It multiples corresponding translation probabilities and lexical weights in source-pivot and pivot-target translation models to induce a new source-target phrase table.

Based on these two models, we induce a source-target translation model, in which two important elements need to be induced: phrase translation probability and lexical weight.

Phrase Translation Probability We induce the phrase translation probability by assuming the independence between the source and target phrases when given the pivot phrase.

(2003), there are two important elements in the lexical weight: word alignment information a in a phrase pair (5, f) and lexical translation probability w (s

Additionally phrase translation probabilities need to be estimated accurately, which means sentences that offer phrases whose occurrences in the corpus were rare are informative.

estimating accurately the phrase translation probabilities .

Smoothing techniques partly handle accurate estimation of translation probabilities when the events occur rarely (indeed it is the main reason for smoothing).