Word Embeddngs 사용 사례 캡쳐

출처 : https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/

3. Word Embeddings use case scenarios

Since word embeddings or word Vectors are numerical representations of contextual similarities between words, they can be manipulated and made to perform amazing tasks like-

1. Finding the degree of similarity between two words.
   model.similarity('woman','man')
0.73723527
2. Finding odd one out.
   model.doesnt_match('breakfast cereal dinner lunch';.split())
'cereal'
3. Amazing things like woman+king-man =queen
   model.most_similar(positive=['woman','king'],negative=['man'],topn=1)
queen: 0.508
4. Probability of a text under the model
   model.score(['The fox jumped over the lazy dog'.split()])
0.21

Below is one interesting visualisation of word2vec.

The above image is a t-SNE representation of word vectors in 2 dimension and you can see that two contexts of apple have been captured. One is a fruit and the other company.