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Authors: Rui Zhang ; Peter Schneider-Kamp and Arthur Zimek

Affiliation: Mathematics & Computer Science, University of Southern Denmark, Campusvej 55, Odense, Denmark

Keyword(s): Natural Language Processing, Post-processing Model, Retrofitting, Word Representations, Knowledge-based Sense Representations, Negative Sampling, Semantic Similarity.

Abstract: This paper presents an approach for retrofitting pre-trained word representations into sense level representations to improve semantic distinction of words. We use semantic relations as positive and negative examples to refine the results of a pre-trained model instead of integrating them into the objective functions used during training. We experimentally evaluate our approach on two word similarity tasks by retrofitting six datasets generated from three widely used techniques for word representation using two different strategies. Our approach significantly and consistently outperforms three state-of-the-art retrofitting approaches.

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Paper citation in several formats:
Zhang, R.; Schneider-Kamp, P. and Zimek, A. (2020). Improving Semantic Similarity of Words by Retrofitting Word Vectors in Sense Level. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 108-119. DOI: 10.5220/0008953001080119

@conference{icaart20,
author={Rui Zhang. and Peter Schneider{-}Kamp. and Arthur Zimek.},
title={Improving Semantic Similarity of Words by Retrofitting Word Vectors in Sense Level},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={108-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008953001080119},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Improving Semantic Similarity of Words by Retrofitting Word Vectors in Sense Level
SN - 978-989-758-395-7
IS - 2184-433X
AU - Zhang, R.
AU - Schneider-Kamp, P.
AU - Zimek, A.
PY - 2020
SP - 108
EP - 119
DO - 10.5220/0008953001080119
PB - SciTePress

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