Technical Column Author: Yang Hangfeng Editor: Zhang Nimei 1.Word2Vec Overview Word2Vec is simply a method of representing the semantic information of words through learning from text and using word vectors, that is, mapping the original word space to a new space through Embedding, so that semantically similar words are close to each other in this … Read more
1 Introduction This article mainly introduces a very classic algorithm in word embedding, Word2Vec. Initially, Word2Vec was primarily used in text-related problems, but now friends participating in competitions should have noticed that almost half of the traditional data competitions involve Word2Vec. Therefore, we must take a good look at what Word2Vec is actually learning, so … Read more
1 Meaning of Word2Vec A word cannot be understood by a neural network; it needs to be converted into numbers before being fed into it. The most naive way is one-hot encoding, but it is too sparse and not effective. So we improve it by compressing one-hot into a dense vector. The word2vec algorithm predicts … Read more
Introduction References Main Content Dataset Model Training Model Evaluation Model Tuning Extensions Bonus Introduction Hello everyone, I am a dropout from Royal Bruster University of Data Mining, I drink the strongest orange juice and dig the deepest corners—persistent as I am. Last week, I impulsively dug a big pit of Word2vec, leaving the practical part … Read more
Authorized by WeChat account Data Mining Machine Cultivation Diary Author | Mu Wen This article is exclusively authorized for reprint by “Big Data Digest” and prohibits all other forms of reprint without the author’s permission. Hello everyone, my name is Data Mining Machine, I dropped out of Royal Bruster University, I drink the strongest orange … Read more
Click the “MLNLP” above, and select “Star” to follow the public account Heavyweight content delivered first-hand Editor: Yizhen https://www.zhihu.com/question/321088108 This article is for academic exchange and sharing. If there is any infringement, it will be deleted. The author found an interesting question on Zhihu titled “Why can negative sampling in word2vec achieve results similar to … Read more
Translation | Yu Zhipeng Lin Xiao Proofreading | Cheng Sijie Compiled | Kong Lingshuang | AI Study Group Introduction The Word2Vec model is used to learn vector representations of words, which we call “word embeddings”. Typically, it serves as a preprocessing step, after which the word vectors are fed into a discriminative model (usually RNN) … Read more
Click on Machine Learning Algorithms and Python Learning ,Select Star Exciting content won’t get lost Source: Big Data Digest Embedding is one of the most fascinating ideas in machine learning. If you’ve ever used Siri, Google Assistant, Alexa, Google Translate, or even your smartphone keyboard for next-word prediction, you have likely benefited from this concept, … Read more
Click the image below to get the knowledge card Reading Difficulty: ★★☆☆☆ Skill Requirements: Machine Learning, Python, Tokenization, Data Visualization Word Count: 1500 words Reading Time: 6 minutes This article combines the recently popular TV series “Yanxi Palace” to analyze the character relationships from a data perspective. By collecting relevant novels, scripts, character introductions, etc., … Read more
OpenNLP is a natural language processing toolkit developed by the Apache Foundation, providing a range of machine learning tools for processing natural language text. It supports the most common NLP tasks, such as tokenization, sentence detection, part-of-speech tagging, named entity recognition, and more. Core Advantages Complete Functionality: Covers most basic NLP tasks Easy Integration: Can … Read more