Cross-lingual Lexical Sememe Prediction (2018-01-01T00:00:00.000000Z)

TL;DR

This work proposes a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction, and shows that the proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingUAL sememe Prediction.

Abstract

Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at https://github.com/thunlp/CL-SP.

Authors

Zhiyuan Liu

34 papers

Yankai Lin

9 papers

Maosong Sun

46 papers

TL;DR

Abstract

Authors

References46 items

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