Advancements in Word Embeddings: A Comprehensive Survey and Analysis

Abstract

In recent years, the field of Natural Language Processing (NLP) has seen significant growth in the study of word representation, with word embeddings proving valuable for various NLP tasks by providing representations that encapsulate prior knowledge. We reviewed word embedding models, their applications, cross-lingual embeddings, model analyses, and techniques for model compression. We offered insights into the evolving landscape of word representations in NLP, focusing on the models and algorithms used to estimate word embeddings and their analysis strategies. To address this, we conducted a detailed examination and categorization of these evaluations and models, highlighting their significant strengths and weaknesses. We discussed a prevalent method of representing text data to capture semantics, emphasizing how different techniques can be effectively applied to interpret text data. Unlike traditional word representations, such as Word to Vector (word2vec), newer contextual embeddings, like Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo), have pushed the boundaries by capturing the use of words through diverse contexts and encoding information transfer across different languages. These embeddings leverage context to represent words, leading to innovative applications in various NLP tasks.