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Large tagset labeling using Feed Forward Neural Networks. Case study on Romanian Language

Tiberiu Boros, Radu Ion and Dan Tufis

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013


Standard methods for part-of-speech tagging suffer from data sparseness when used on highly inflectional languages (which require large lexical tagset inventories). For this reason, a number of alternative methods have been proposed over the years. One of the most successful methods used for this task, called Tiered Tagging (TufiƟ, 1999), exploits a reduced set of tags derived by removing several recoverable features from the lexicon morpho-syntactic descriptions. A second phase is aimed at recovering the full set of morpho-syntactic features. In this paper we present an alternative method to Tiered Tagging, based on local optimizations with Neural Networks and we show how by properly encoding the input sequence in a general Neural Network architecture, we achieve results similar to the Tiered Tagging methodology, significantly faster and without requiring extensive linguistic knowledge as implied by the previously mentioned method.

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