This paper presents a method of lexical semantic disambiguation in multilingual corpora and describes the construction of an artificial word-aligned and lexically disambiguated gold-standard corpus from an existing multilingual resource. The suggested approach uses sets of aligned words and phrases across languages as unique semantic tags similar to WordNet synsets that can be used as a part of unsupervised natural language processing and information retrieval tasks. The approach goes beyond one-to-one word alignment, and uses an algorithm for the aggregation of results of pair-wise word alignment when the corpus contains several languages. When applied to the new corpus, this methodology has proven capable of reducing the ambiguity of a polysemous word by one third on average.
['Dimitar Kazakov', 'Ahmad R. Shahid']
Using Parallel Corpora for Word Sense Disambiguation