This paper presents a language-independent approach for morphological
disambiguation which has been regarded as an extension of POS tagging, jointly
predicting complex morphological tags. In the proposed approach, all words, roots,
POS and morpheme tags are embedded into vectors, and context representations from
surface word and morphological contexts are calculated. Then the inner products
between analyses and the context’s representations are computed to perform the
disambiguation. The underlying hypothesis is that the correct morphological analysis
should be closer to the context in a vector space. Experimental results show that the
proposed approach out-performs the existing models on seven different language
datasets. Concretely, compared with the baselines of MarMot and a sophisticated
neural model (Seq2Seq), the proposed approach achieves around 6% improvement in
average accuracy for all languages while running about 6 and 33 times faster than
MarMot and Seq2Seq, respectively.

Language-Independent Approach for Morphological Disambiguation

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VPS3 - Morphosyntax, tagging, Chunking, and Parsing

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Alymzhan Toleu

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Language-Independent Approach for Morphological Disambiguation

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