Understanding Sentence Similarity in NLP: Top 3 Models You Should Know

Sentence Similarity, is a specific task within field of Natural Language Processing (NLP) that involves assessing how similar two sentences are in terms of meaning, structure, or both. This can be useful for various applications like identifying paraphrases, clustering similar documents, or finding relevant search results.

Use cases

Typical NLP Tasks Involving Sentence Similarity are:

• Paraphrase Detection: Determine if two sentences are paraphrases of each other.
• Textual Entailment: Assess if one sentence logically follows from another.
• Semantic Textual Similarity (STS): Measure the degree to which two sentences express the same meaning, usually resulting in a continuous score.
• Question Answering: Match user queries with relevant answers by comparing the similarity between the question and potential answers.
• Information Retrieval: Rank documents or snippets by how closely they match a query based on sentence similarity.

Top 3 Sentence Similarity Models

Here are the top 3 models which are open sourced and trending in Sentence Similarity field:

1 - sentence-transformers