Calculating Document Similarities using BERT, word2vec, and other models

 By Medium - 2020-12-03

Description

Document similarities is one of the most crucial problems of NLP. Finding similarity across documents is used in several domains such as recommending similar books and articles, identifying…

Summary

  • Document similarities is one of the most crucial problems of NLP.
  • We can train our own embeddings if have enough data and computation available or we can use pre-trained embeddings.
  • Using this embedding we can convert every word of our document corpus into a 300-dimensional vector.
  • Documents similar to first document based on cosine similarity and euclidean distance (Image by author) BERT- Bidirectional Encoder Representation from Transformers (BERT) is a state of the art technique for natural language processing pre-training developed by Google.

 

Topics

  1. NLP (0.27)
  2. Machine_Learning (0.06)
  3. Backend (0.04)

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