Word2vec

Essential Algorithms for Deep Learning: Word2Vec (Part 1)

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Essential Algorithms for Deep Learning: Word2Vec (Part 1)

Author: milter Link: https://www.jianshu.com/p/1405932293ea Word2Vec has become a foundational algorithm in the field of NLP. As an AI practitioner, you should feel embarrassed if you do not actively familiarize yourself with this algorithm. This article is a translation, and the original link is: http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/If your English is good, I strongly recommend reading the original text … Read more

YAGO Founder Fabian: Knowledge Base II (Deep Learning in NLP)

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YAGO Founder Fabian: Knowledge Base II (Deep Learning in NLP)

On July 18th from 13:30 to 16:30, Professor Fabian M. Suchanek, founder of the YAGO knowledge base and a professor at Télécom Paris, delivered the second report related to the knowledge base for this summer school via remote access, focusing on deep natural language processing. The report detailed the applications of deep learning in natural … Read more

Six Common Patterns of Text Vectorization

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Six Common Patterns of Text Vectorization

Source: Machine Learning AI Algorithm Engineer This article is approximately 1000 words, and it is recommended to read in 5minutes. This article introduces six common patterns of text vectorization. 1. Text Vectorization Text vectorization: representing text information as vectors that can express the semantics of the text, using numerical vectors to represent the semantics of … Read more

Understanding Embedding in Neural Network Algorithms

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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

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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

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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

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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

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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

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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

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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