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LAMB: A Good Shepherd of Morphologically Rich Languages

Sebastian Ebert,Thomas Müller,Hinrich Schütze

2016 · DOI: 10.18653/v1/D16-1071
Conference on Empirical Methods in Natural Language Processing · 8 Citations

TLDR

This paper introduces S TEM and L AMB, embeddings trained for stems and lemmata instead of for surface forms for morphologically rich languages, and shows that bothembeddings have high quality even for small dimensionality and training corpora.

Abstract

This paper introduces S TEM and L AMB , embeddings trained for stems and lemmata instead of for surface forms. For morphologically rich languages, they perform significantly better than standard embeddings on word similarity and polarity evaluations. On a new WordNet-based evaluation, S TEM and L AMB are up to 50% better than standard embeddings. We show that both embeddings have high quality even for small dimensionality and training corpora.