Understanding Embedding in Neural Network Algorithms

This article will explainthe essence of Embedding, the principle of Embedding,and the applications of Embedding in three aspects, helping you understand Embedding. 1.Essence of Embedding “Embedding” literally translates to “embedding”, but in the context of machine learning and natural language processing, we prefer to understand it as a technique of “vectorization” or “vector representation”, which … Read more

Method Sharing: Text Analysis Using Word Embedding

Method Sharing: Text Analysis Using Word Embedding

Introduction Text analysis has traditionally been dominated by qualitative methods, with the two most common being interpretive close reading and systematic qualitative coding. Both are limited by human reading speed, making them unsuitable for analyzing extremely large corpora. Currently, two popular quantitative text analysis methods are semantic network analysis and topic modeling. While both make … Read more

The Intricate Mathematics Behind Word Vectors

The Intricate Mathematics Behind Word Vectors

Follow the WeChat public account “ML_NLP“ Set as “Starred“, heavy content delivered to you first! From | Zhihu Address | https://zhuanlan.zhihu.com/p/270210535 Author | Pan Xiaoxiao Editor | Machine Learning Algorithms and Natural Language Processing WeChat public account This article is for academic sharing only. If there is any infringement, please contact us to delete the … Read more

Understanding Self-Supervised Learning

Understanding Self-Supervised Learning

Self-Supervised Learning is a popular research area in recent years. It aims to extract the inherent representation features of unlabeled data by designing auxiliary tasks as supervisory signals, thereby enhancing the model’s feature extraction capabilities. Today, let’s explore what self-supervised learning is! 01 What is Self-Supervised Learning? Machine learning can be classified into supervised learning, … Read more

From Text Matching to Semantic Relevance

From Text Matching to Semantic Relevance

Introduction Text similarity is a fundamental task in the industrialization of NLP. Many applications require calculating the degree of similarity between two texts, including deduplication of similar texts in text retrieval, matching queries with standard template questions in question-answering systems, and semantic judgment of sentence pairs. This task can be categorized based on different criteria: … Read more

Understanding Embedding in Language Models

Understanding Embedding in Language Models

Original: https://zhuanlan.zhihu.com/p/643560252 Like most people, my understanding of natural language processing and language models began with ChatGPT. Like most people, I was shocked by ChatGPT’s capabilities upon first contact — silicon-based intelligence has indeed achieved understanding human language. I also had the almost universal question: how is this achieved? Does the potential of silicon-based intelligence … Read more

5-Minute NLP Series: Word2Vec and Doc2Vec

5-Minute NLP Series: Word2Vec and Doc2Vec

Source: Deephub Imba This article is approximately 800 words long and is recommended to be read in 5 minutes. This article mainly introduces <strong>Word2Vec</strong> and <strong>Doc2Vec</strong>. Doc2Vec is an unsupervised algorithm that learns embeddings from variable-length text segments (such as sentences, paragraphs, and documents). It first appeared in the paper Distributed Representations of Sentences and … Read more

Performance Comparison of Text Embedding Techniques: GPT-3, BERT, GloVe, and Word2Vec

Performance Comparison of Text Embedding Techniques: GPT-3, BERT, GloVe, and Word2Vec

Source: DeepHub IMBA This article is about 3000 words long and is recommended to read in 6 minutes. With the latest advancements in NLP (Natural Language Processing), OpenAI's GPT-3 has become one of the most powerful language models on the market. On January 25, 2022, OpenAI announced an embedding endpoint (Neelakantan et al., 2022). This … Read more

Understanding Word2Vec: A Deep Dive into Word Vectors

Understanding Word2Vec: A Deep Dive into Word Vectors

Summary of Word2vec References First, let me briefly describe my deep dive into Word2vec: as per usual, I started by reading Mikolov’s two original papers on Word2vec, but I found myself still confused after finishing them. The main reason is that these two papers omitted too much theoretical background and derivation details. I then dug … Read more

Deep Learning Text Representation Models

Deep Learning Text Representation Models

Source: Poll’s Notes Original URL:http://www.cnblogs.com/maybe2030/ Reading Directory 1. Word Vectors 2. Distributed Representation of Word Vectors 3. Word Vector Models 4. Word2Vec Algorithm Concepts 5. Doc2Vec Algorithm Concepts 6. References Deep learning has opened a new chapter in machine learning, and significant breakthroughs have been made in applying deep learning to images and speech. Deep … Read more