NeuMorph: Neural Morphological Tagging for Low-Resource Languages—An Experimental Study for Indic Languages

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427037" target="_blank" >RIV/00216208:11320/19:10427037 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3342354" target="_blank" >https://doi.org/10.1145/3342354</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
NeuMorph: Neural Morphological Tagging for Low-Resource Languages—An Experimental Study for Indic Languages
Original language description
This article deals with morphological tagging for low-resource languages. For this purpose, five Indic languages are taken as reference. In addition, two severely resource-poor languages, Coptic and Kurmanji, are also considered. The task entails prediction of the morphological tag (case, degree, gender, etc.) of an in-context word. We hypothesize that to predict the tag of a word, considering its longer context such as the entire sentence is not always necessary. In this light, the usefulness of convolution operation is studied resulting in a convolutional neural network (CNN) based morphological tagger. Our proposed model (BLSTM-CNN) achieves insightful results in comparison to the present state-of-the-art. Following the recent trend, the task is carried out under three different settings: single language, across languages, and across keys. Whereas the previous models used only character-level features, we show that the addition of word vectors along with character-level embedding significantly improves the performance of all the models. Since obtaining high-quality word vectors for resource-poor languages remains a challenge, in that scenario, the proposed character-level BLSTM-CNN proves to be most effective.1
Czech name
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Czech description
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Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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