Cross-Lingual Word Sense Disambiguation for Languages with Scarce Resources

Word Sense Disambiguation has long been a central problem in computational linguistics. Word Sense Disambiguation is the ability to identify the meaning of words in context in a computational manner. Statistical and supervised approaches require a large amount of labeled resources as training datasets. In contradistinction to English, the Persian language has neither any semantically tagged corpus to aid machine learning approaches for Persian texts, nor any suitable parallel corpora. Yet due to the ever-increasing development of Persian pages in Wikipedia, this resource can act as a comparable corpus for English-Persian texts.

In this paper, we propose a cross-lingual approach to tagging the word senses in Persian texts. The new approach makes use of English sense disambiguators, the Wikipedia articles in both English and Persian, and a newly developed lexical ontology, FarsNet. It overcomes the lack of knowledge resources and NLP tools for the Persian language. We demonstrate the effectiveness of the proposed approach by comparing it to a direct sense disambiguation approach for Persian. The evaluation results indicate a comparable performance to the utilized English sense tagger.