Word2Vec
Word2Vec represents the words as high-dimensional vectors so that we get semantically similar words close to each other in the vector space. There are two main architectures for Word2Vec:
- Continuous Bag of Words: The objective is to predict the target word based on the context of surrounding words.
- Skip-gram: The model is designed to predict the surrounding words in the context.
Different Techniques for Sentence Semantic Similarity in NLP
Semantic similarity is the similarity between two words or two sentences/phrase/text. It measures how close or how different the two pieces of word or text are in terms of their meaning and context.
In this article, we will focus on how the semantic similarity between two sentences is derived. We will cover the following most used models.
- Dov2Vec – An extension of word2vec
- SBERT – Transformer-based model in which the encoder part captures the meaning of words in a sentence.
- InferSent -It uses bi-directional LSTM to encode sentences and infer semantics.
- USE (universal sentence encoder) – It’s a model trained by Google that generates fixed-size embeddings for sentences that can be used for any NLP task.
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