Unveiling the Powerhouses of Contextualized Representations: Hybrid Word Embeddings vs Graph-Based Representations
In the realm of natural language processing (NLP), capturing context is crucial for accurate understanding and effective modeling. Two prominent approaches have emerged to tackle this challenge: Hybrid Word Embeddings and Graph-Based Representations. While both methods aim to improve representation learning, they differ in their underlying philosophy and implementation.
Hybrid Word Embeddings: A Blend of Strengths
Hybrid word embeddings, such as Subword-based Word Embeddings (SUBWORDS) and WordPiece embeddings, combine the strengths of traditional word2vec and subword-based models. These hybrids leverage the strengths of both worlds, where word2vec captures semantic relationships and subword-based models handle out-of-vocabulary (OOV) words and morphological variations. For instance, a hybrid model might represent "running" as a combination of "run" and "ing"...
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