Source: AI Study Society I was interviewed recently and, since I still don’t fully understand how word embeddings work, I’ve been looking for a lot of related materials to grasp this concept better. My understanding is still limited, so I won’t overestimate myself by writing my own article (even if I did, it would just … Read more
[Introduction] Embedding representation learning is a current research hotspot. From Word2Vec to Node2Vec to Graph2Vec, a large number of X2Vec algorithms have emerged. But how can we construct a theory of vector embeddings to guide algorithm design? Recently, Professor Martin Grohe, a computer science professor at RWTH Aachen University and ACM Fellow, gave a report … Read more
Deep learning has achieved great success in natural language processing (NLP) tasks, among which distributed representation of words is a crucial technology. To deeply understand distributed representation, one must delve into word2vec. Today, let’s explore how the Huffman Tree is generated in the word2vec code. This is a very important data structure in word2vec, used … Read more
Since Tomas Mikolov from Google proposed Word2Vec in “Efficient Estimation of Word Representation in Vector Space”, it has become a fundamental component of deep learning in natural language processing. The basic idea of Word2Vec is to represent each word in natural language as a short vector with a unified meaning and dimension. As for what … Read more
Summary of Word2vec Reference Materials Let me briefly describe my deep dive into Word2vec: I first looked at Mikolov’s two original papers on Word2vec, but found myself still confused after reading them. The main reason is that these papers omit too much theoretical background and derivation details. I then revisited Bengio’s 2003 JMLR paper and … Read more
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
Source | Wujie Community Mixlab Editor | An Ke 【PanChuang AI Introduction】: Recently, everyone has been flooded with the popular Qing Dynasty drama “Story of Yanxi Palace”~ The male lead, Emperor Qianlong, is often referred to as a “big pig’s hoof” by everyone because he falls in love with every woman he meets. As simple … Read more
Python Natural Language Processing Tool: Word2Vec from Beginner to Practical Hello everyone, I am Niu Ge! Today, I will take you deep into understanding Word2Vec, a very important tool in the field of natural language processing. With it, we can enable computers to truly “understand” the relationships between words, achieving smarter text processing. What is … Read more
I’ve been looking at word2vec for a long time, but I’ve found many different versions of explanations. Moreover, the original paper does not mention many details, so I plan to look at the source code directly. On one hand, it can deepen my understanding; on the other hand, I can make appropriate improvements in the … Read more
Natural Language Processing Author: Zhang Junlin Source: Deep Learning Frontier Notes Zhihu Column Original Link: https://zhuanlan.zhihu.com/p/49271699 The theme of this article is the pre-training process in natural language processing (NLP). It will roughly explain how pre-training techniques in NLP have gradually developed into the BERT model, naturally illustrating how the ideas behind BERT were formed, … Read more