The sense similarity scores are computed by using the vector space model.

Similarity scores are used as additional features of the translation model to improve translation performance.

In Alg2, the similarity score consists of three parts as in Equation (14): sim(Cf“”,C;""c) , sim(Cf‘”,C§""c) , and sim(C§°oc,C:""C) ; where sim(CJf.0°C,C:0“) could be computed by IBM model 1 probabilities simIBM(C;0“,C:OOC) or cosine distance similarity function simCOS(C;OOC,C:W) .

The monolingual similarity scores give it the ability to avoid “dangerous” words, and choose alternatives (such as larger phrase translations) when available.

We then combine the two similarity scores by using both of them as features to see if we could obtain further improvement.

The sense similarity scores are used as feature functions in the translation model.

Our algorithm introduces nonaligned signatures (NAS), a cross-lingual word context similarity score that avoids the over-constrained and inefficient nature of alignment-based methods.

We now rank the candidates according to the nonaligned signatures (NAS) similarity score , which assesses the similarity between each candidate’s signature and that of the headword.

3.4 Nonaligned Signatures (NAS) Similarity Scoring

At the heart of our method is the nonaligned signatures (NAS) context similarity score , used for removing incorrect translations using cross-lingual co-occurrences.

The common method for context similarity scoring utilizes some algebraic distance between context vectors, and requires a single alignment of context vectors in one language into the other.

We present the nonaligned signatures (NAS) similarity score for signature and use it to rank these translations.

In this way, the two scores are ‘plugged’ into our method and serve as baselines for our NAS similarity score .

When computing distributional similarity scores , a template is represented as a feature vector of the CUIs that instantiate its arguments.

Next, we represent each pair of propositional templates with a feature vector of various distributional similarity scores .

A template pair is represented by a feature vector where each coordinate is a different distributional similarity score .

We then generate for any (t1, t2) features that are the 12 distributional similarity scores using all combinations of the dimensions.