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Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings
We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seven languages.
An Unsupervised Approach for Mapping between Vector Spaces
Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language.
Exploiting Morphological Regularities in Distributional Word Representations
We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces.
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations.
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