Integrating Approaches to Word Representation

TL;DR

This paper surveys various methods for representing words in neural NLP systems, focusing on distributional, compositional, and relational approaches, and discusses integration techniques especially for out-of-vocabulary words.

Contribution

It provides a comprehensive overview of different word representation approaches and explores how to effectively combine them in neural systems.

Findings

Comparison of distributional, compositional, and relational methods

Discussion on integrating approaches for improved word representation

Analysis of handling out-of-vocabulary words

Abstract

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.