In this work, we explore generating morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. We present here the first-ever word analogy dataset for Tamil, consisting of 4499 hand-curated word tetrads across 10 semantic and 13 morphological relation types. Using a rules-based morphological segmenter and meta-embedding techniques, we train meta-embeddings that outperform existing baselines by 16% on our analogy task and appear to mitigate a previously observed trade-off between semantic and morphological accuracy.

Morphology-Aware Meta-Embeddings for Tamil



<i>**Session Chair: ** Nelson Liu </i><br><br>

3F-Oral: Student Research Workshop 2

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Seyoon Ragavan

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Morphology-Aware Meta-Embeddings for Tamil

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