Text **embedding**
[Text Tokenizer](https://texonom.com/text-tokenizer-2bbc41eaa76c4674a4f4b9127fbe5da1) [Latent Space](https://texonom.com/latent-space-d67b6bdef18b4058bfbc3d25f87ec087) [Text Encoding](https://texonom.com/text-encoding-ab7377cfc5c648059de4860510ad9134) [Recommend System](https://texonom.com/recommend-system-e94b364527554e65bfd9d3dc63fb1af7) 


### Word string → Tokenizer → Token → Embedding layer → Dense vector
텍스트 분석을 위해 단어를 표현하는 데 사용되는 용어
벡터 공간에서 더 가까운 단어가 예상되도록 단어의 의미를 인코딩하는 실수 벡터의 형태
원-핫 벡터는 단어 벡터 간 유의미한 유사도를 계산할 수 없다는 단점
### Text **embedding Notion**
|Title|
|:-:|
|[Positional Encoding](https://texonom.com/positional-encoding-19f7bc463c34450987b69ee4b610a08b)|
|[Token Embeddings](https://texonom.com/token-embeddings-deca26ce26df47f190b4fdabfa8ac973)|
|[Measures of Contextuality](https://texonom.com/measures-of-contextuality-b3a0443e9489424f82ce4bc19dd1014c)|


### Static **embedding Methods**
|Title|
|:-:|
|[Glove Embedding](https://texonom.com/glove-embedding-7356854e1b0e4aff8142c2fadd24070c)|
|[Bag of words](https://texonom.com/bag-of-words-83c7dd03d30d4ed99c8130ba319fe484)|
|[TF-IDF](https://texonom.com/tf-idf-e564d1cb86074a7fa2f46c38cab552b2)|
|[Word2Vec](https://texonom.com/word2vec-b3b6fa1a01324dd2b10109bdeb192711)|


### Contextual **embedding Methods**
|Title|
|:-:|
|[Sentence Transformer](https://texonom.com/sentence-transformer-5b7338c829c748d49960e592ef4ba8db)|
|[ELMo](https://texonom.com/elmo-6e0418bc10274d29b2cb7a1aae8993a4)|



> [Contextual Embedding - How Contextual are Contextualized Word Representations?](https://velog.io/@stapers/Contextual-Embedding-How-Contextual-are-Contextualized-Word-Representations)

> [Embedding vs Encoding](https://beausty23.tistory.com/223)

> [[딥러닝] 언어모델, RNN, GRU, LSTM, Attention, Transformer, GPT, BERT 개념 정리](https://velog.io/@rsj9987/딥러닝-용어정리)





