A motivation for this augmentation is that semantic tags allow to match tokens that do not occur in the original bilingual parallel corpora used for phrase table extraction.

Like lexical phrase tables, SPTs are extracted from parallel corpora .

As a first step we annotate the parallel corpora with named-entity taggers for the source and target languages, replacing named entities with general semantic labels chosen from a coarse-grained taxonomy (person, location, organization, date and numeric expression).

CLTE has been previously modeled as a phrase matching problem that exploits dictionaries and phrase tables extracted from bilingual parallel corpora to determine the number of word sequences in H that can be mapped to word sequences in T. In this way a semantic judgement about entailment is made exclusively on the basis of lexical evidence.

To build the English-German phrase tables we combined the Europarl, News Commentary and “de-news”3 parallel corpora .

The dictionary created during the alignment of the parallel corpora provided the lexical knowledge to perform matches when the connected words are different, but semantically equivalent in the two languages.

In order to build the English-Spanish lexical phrase table (PT), we used the Europarl, News Commentary and United Nations parallel corpora .

We construct our supervised WSD system directly from parallel corpora .

To generate the WSD training data, 7 parallel corpora were used, including Chinese Treebank, FBIS Corpus, Hong Kong Hansards, Hong Kong Laws, Hong Kong News, Sinorama News Magazine, and Xinhua Newswire.

Then, word alignment was performed on the parallel corpora with the GIZA+ + software (Och and Ney, 2003).