Coarse Semantic Classification of Rare Nouns Using Cross-Lingual Data and Recurrent Neural Networks

The paper presents a method for WordNet supersense tagging of Sanskrit, an ancient Indian language with a corpus grown over four millenia. The proposed method merges lexical information from Sanskrit texts with lexicographic definitions from Sanskrit-English dictionaries, and compares the performance of two machine learning methods for this task. Evaluation concentrates on Vedic, the oldest layer of Sanskrit. This level of Sanskrit contains numerous rare words that are no longer used in the later language and whose word senses can, therefore, not be induced from their occurrences in other texts. The paper studies how to efficiently transfer knowledge from later forms of Sanskrit and from modern Western dictionaries for this special task of supersense disambiguation.