Most existing word embedding models use a vast corpus as a primary source while training, but this requires massive time and space for data prepossessing and model training. 
We propose a new model, HG2Vec, that learns word embeddings utilizing only dictionaries and thesauri.
We utilize dictionaries and thesauri as the only resources (without using any text corpus) to train word embeddings and obtain state-of-art results on several benchmarks.
We demonstrate that learning synonyms and antonyms is necessary for word embedding models, so a model can distinguish the words with opposite meanings that appear in similar contexts.
We propose a context-focused loss to boost learning in closely related contexts

HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph

If you are a virtual attendee please click on the button above to join the live session **Chairs:** Marc Verhagen, Peter Anick and Zhongqing Wang

VPS3 - Language Modeling

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Qitong Wang

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SHORT BIO

Presentations

HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph