Integrating Approaches to Word Representation
arXiv:2109.04876v15 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental survey for NLP researchers, summarizing existing methods without introducing new solutions.
The paper surveys distributional, compositional, and relational approaches to word representation in natural language processing, focusing on integration methods and addressing out-of-vocabulary issues.
The problem of representing the atomic elements of language in modern neural learning systems is one of the central challenges of the field of natural language processing. I present a survey of the distributional, compositional, and relational approaches to addressing this task, and discuss various means of integrating them into systems, with special emphasis on the word level and the out-of-vocabulary phenomenon.