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
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
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
Source: DeepHub IMBA This article is approximately 1000 words long and is recommended to be read in 5 minutes. This article will provide a complete summary of word embedding models. TF-IDF, Word2Vec, GloVe, FastText, ELMO, CoVe, BERT, RoBERTa The role of word embeddings in deep models is to provide input features for downstream tasks (such … Read more
Follow the public account “ML_NLP“ Set as “Starred“, delivering heavy content promptly! Author: Shining School: Beijing University of Posts and Telecommunications Original article link: https://www.cnblogs.com/gczr/p/12874409.html 1. Background Introduction BERT and RoBERTa have achieved SOTA results in regression tasks for sentence pairs, such as text semantic similarity. However, they require feeding both sentences into the network … Read more
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
Written by丨Mony Researcher at Tencent Interactive Entertainment // Introduction As we know, we are deeply caught in the whirlpool of an artificial intelligence (AI) revolution. No matter which industry AI intervenes in, it brings revolutionary hopes and possibilities, while also triggering brand new challenges. For applications including large language models, generative AI, and semantic search, … Read more
This article is a joint introduction to computer industry terminology launched by the CCF Computer Terminology Review Committee and the CCF Natural Language Processing Special Committee. The hot word selected for this issue is Unsupervised Machine Translation, which is one of the current popular research directions. Unsupervised machine translation methods no longer rely on large-scale … Read more