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MorphIRe aims to enhance natural language processing for morphologically-rich languages by developing representations based on morphemes, the smallest grammatical units. The project uses deep learning techniques to improve performance in various NLP tasks, ultimately benefiting speakers of these languages with better t…
The morphological structure of a word plays an important role in determining its function and meaning, yet it is often disregarded by current machine learning models aimed at natural language processing (NLP).
State-of-the-art NLP models typically rely on word-level or character-level representations.
This arguably works well for English, the dominant language in NLP research, since it is morphologically simple, but poses a challenge for morphologically-rich languages like Basque, Estonian, or K…
KOBENHAVNS UNIVERSITET
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