Field Embedding: A Unified Grain-Based Framework for Word Representation

21734

Authors

nan

Abstract

AbstractWord representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings. Current methods mainly focus on learning embeddings for words while embeddings of linguistic information (referred to as grain embeddings) are discarded after the learning. This work proposes a framework field embedding to jointly learn both word and grain embeddings by incorporating morphological, phonetic, and syntactical linguistic fields. The framework leverages an innovative fine-grained pipeline that integrates multiple linguistic fields and produces high-quality grain sequences for learning supreme word representations. A novel algorithm is also designed to learn embeddings for words and grains by capturing information that is contained within each field and that is shared across them. Experimental results of lexical tasks and downstream natural language processing tasks illustrate that our framework can learn better word embeddings and grain embeddings. Qualitative evaluations show grain embeddings effectively capture the semantic information.

Keywords

nan

Full Content

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1754–1762 June 6–11, 2021. ©2021 Association for Computational Linguistics 1754 �� 1,2∗� �� 1∗†� �� 1∗� �� 1� �� 3� �� 4 1�� 2�� 3�� 4�� 智 �� …

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Publication Details

Paper ID:: 21734
Year:: 2021
Publication:: TIPSTER
Publication Type:: conf
DOI:: nan

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