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Advances in Pre-Training Distributed Word Representations

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arxiv 1712.09405 v1 pith:IVCXN2JM submitted 2017-12-26 cs.CL

Tomas Mikolov , Edouard Grave , Piotr Bojanowski , Christian Puhrsch , Armand Joulin This is my paper
classification cs.CL
keywords representationswordlargepre-trainedadvancesapplicationsavailablecollections
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Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks.

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  1. Edeflip: Supervised Word Translation between English and Yoruba

    cs.CL 2025-06 conditional novelty 4.0 of 10

    For English-Yoruba word translation, normalization greatly improves precision for curated embeddings but only slightly for Wikipedia embeddings.

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