Joint learning of morphology and syntax with cross-level contextual information flow



Can B, Aleçakır H, Manandhar S & Bozşahin C (2022) Joint learning of morphology and syntax with cross-level contextual information flow. Can Buglalilar B (Project Leader) Natural Language Engineering, 28 (6), pp. 763-795.

We propose an integrated deep learning model for morphological segmentation, morpheme tagging, part-of-speech (POS) tagging, and syntactic parsing onto dependencies, using cross-level contextual information flow for every word, from segments to dependencies, with an attention mechanism at horizontal flow. Our model extends the work of Nguyen and Verspoor (2018) on joint POS tagging and dependency parsing to also include morphological segmentation and morphological tagging. We report our results on several languages. Primary focus is agglutination in morphology, in particular Turkish morphology, for which we demonstrate improved performance compared to models trained for individual tasks. Being one of the earlier efforts in joint modeling of syntax and morphology along with dependencies, we discuss prospective guidelines for future comparison.

Artificial Intelligence; Linguistics and Language; Language and Linguistics; Software

Natural Language Engineering: Volume 28, Issue 6

ContributorDr Burcu Can Buglalilar
Publication date30/11/2022
Publication date online20/01/2022
Date accepted by journal08/08/2021
PublisherCambridge University Press (CUP)

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Dr Burcu Can Buglalilar

Dr Burcu Can Buglalilar

Lecturer in Computing Science, Computing Science