NLP Topic 1 Word Embeddings and Sentence Embeddings

最新推荐文章于 2024-06-09 13:23:36 发布

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最新推荐文章于 2024-06-09 13:23:36 发布 · 402 阅读

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本文深入探讨了如何表示单词的意义，从上下文表示到Word2vec框架的介绍，包括其目标和预测函数。词向量通过捕获相似上下文中的单词关系来代表单词意义，解决了传统方法的局限性。

Topic 1 Word Embeddings and Sentence Embeddings

cs224n-2019

lecture 1: Introduction and Word Vectors
lecture 2: Word Vectors 2 and Word Senses
slp
chapter 6: Vector Semantics
ruder.io/word-embeddings
chapter 14: The Representation of Sentence Meaning

语言是信息传递知识传递的载体，
能有效沟通的前提是，双方的知识等同

文章目录

- Topic 1 Word Embeddings and Sentence Embeddings

How to represent the meaning of a word?

meaning: signifier(symbol) <=> signified(idea or thing)
common solution: WordNet, a thesaurus containing lists of synonym sets and hypernyms 同义词和上位词。
缺点：missing new meanings of words, can’t compute accurate word similarity.
solution: representing words as discrete symbols one-hot, but there is curse of dimensionality problem as well as on natural notion of similarity:

Representing words by their context

It should learn to encode similarity in the vectors themselves
词向量的编码目标是把词相似性进行编码，所有优化的目标和实际的使用都围绕在similarity上。类比所有的编码器，都应该清楚编码的目标是什么！
Distributional semantics: A word’s meaning is given by the words that frequently appear close-by.
You shall know a word by the company it keeps.
Word vectors/word embeddings: a dense vector for each word, chosen so that it is similar to vectors of words that appear in similar contexts.