Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

Gábor Berend

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arXiv · 1612.07130

Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

Gábor Berend

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Abstract

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e.~150 sentences per language.

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BibTeX RIS

Gábor Berend. 2016-12-21. Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling. https://arxiv.org/abs/1612.07130

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