Excerpt from Sebastian Ruder Blog Author: Sebastian Ruder Translated by: Machine Heart Contributors: Terrence L This article is Part 3 of the Word Embedding Series, introducing the popular word embedding model Global Vectors (GloVe). To read Part 2, click on Technical | Word Embedding Series Part 2: Comparing Several Methods of Approximate Softmax in Language … Read more
Word embeddings represent a word with a numerical vector, which is different from the IDs used in Tokenization. Word embedding vectors carry more semantic information. This article will illustrate Word2Vec: a method for word embeddings. This series also includes illustrations of Tokenization, Transformer, GPT2, and BERT. If you want to learn about Tokenization, please see … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered to you first! Reprinted from: Machine Learning Beginner 0. Introduction Word embeddings refer to a set of language models and representation learning techniques in Natural Language Processing (NLP). Conceptually, it involves embedding a high-dimensional space of the number of words into a much lower-dimensional … Read more
Analyst Network of Machine Heart Author: Wang Zijia Editor: Joni In this article, the author first introduces the basic knowledge of word2vec to the readers, and then uses six papers as examples to detail how current research utilizes classic word2vec for expansion research. The key focus of the author is the generation process of the … Read more
Selected from arXiv Compiled by Machine Heart Contributors: Li Yazhou, Jiang Siyuan This article starts with two papers to briefly introduce the basic classifications and concepts of Natural Language Processing (NLP), and then showcases NLP in deep learning to the readers. Both papers are excellent introductory reviews, and readers who wish to delve deeper into … Read more