What is Sentence Similarity in AI

Sentence Similarity is a key task in Natural Language Processing (NLP) where the goal is to measure how semantically similar two sentences are. It is widely used in applications such as text clustering, search engines, plagiarism detection, question answering systems, and more.

Sentence Similarity
Figure 1 - Sentence Similarity

How Sentence Similarity Works

The main idea behind sentence similarity is to convert sentences into a numerical format that captures their meaning and context, and then compute the similarity score. In AI, this is often achieved by using various mathematical and deep learning-based techniques.

Traditional Approaches

Traditional approaches for measuring sentence similarity include:

  • Bag of Words (BoW): Treats each sentence as a collection of words without considering order. Similarity is calculated based on the number of common words between sentences.
  • TF-IDF (Term Frequency-Inverse Document Frequency): Represents the importance of words in a sentence by weighting terms based on their frequency across a collection of texts. Similarity is then measured using cosine similarity between vectors.
  • Jaccard Similarity: Measures the similarity between two sets of words by dividing the size of their intersection by the size of their union.

AI-Based Approaches

With advancements in AI, deep learning models have become more powerful and effective for capturing sentence semantics. Some of the AI-based techniques include:

  • Word Embeddings: Pre-trained models such as Word2Vec, GloVe, or FastText convert words into dense vectors that capture semantic meaning. Sentences are represented as the average of word vectors.
  • Sentence Embeddings: Models like Sentence-BERT (SBERT) or Universal Sentence Encoder generate embeddings for entire sentences rather than individual words. This enables capturing the semantic context more effectively.
  • Transformer Models: Modern transformer-based models such as BERT, RoBERTa, and GPT can be fine-tuned to measure sentence similarity. They use attention mechanisms to capture contextual relationships between words in a sentence.

Where can you find Sentence Similarity models

This is the link to use to filter Hunggingface models for Sentence Similarity:

https://huggingface.co/models?pipeline_tag=sentence-similarity&sort=trending

Our favourite Model Authors:

The most interesting Sentence Similarity project

One of the most interesting Sentence Similarity projects is called BGE-M3.

In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.

  • Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
  • Multi-Linguality: It can support more than 100 working languages.
  • Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.

Some suggestions for retrieval pipeline in RAG

We recommend to use the following pipeline: hybrid retrieval + re-ranking.

  • Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings. To use hybrid retrieval, you can refer to Vespa and Milvus.
  • As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. Utilizing the re-ranking model (e.g., bge-reranker, bge-reranker-v2) after retrieval can further filter the selected text.
https://huggingface.co/BAAI/bge-m3

Methods to Calculate Sentence Similarity

Several similarity metrics are commonly used to measure the distance or similarity between sentence embeddings:

  • Cosine Similarity: Measures the cosine of the angle between two sentence vectors. A value of 1 means the sentences are identical, while a value of 0 means they are completely dissimilar.
  • Euclidean Distance: Computes the straight-line distance between two sentence vectors in the vector space.
  • Manhattan Distance: Measures the distance between two vectors by summing the absolute differences of their coordinates.
  • Dot Product: Calculates the dot product of two vectors. Larger values indicate greater similarity.

Applications of Sentence Similarity

Sentence similarity is a fundamental task in many AI and NLP applications:

  • Semantic Search: In search engines, sentence similarity helps rank documents based on their relevance to the search query.
  • Plagiarism Detection: Tools can compare the similarity between two texts to detect plagiarism by calculating the similarity score between sentences or paragraphs.
  • Paraphrase Detection: AI systems use sentence similarity to determine whether two sentences convey the same meaning using different wording.
  • Question Answering: AI-driven question answering systems use sentence similarity to match user queries to the most relevant answers or FAQ entries.
  • Text Summarization: Sentence similarity helps identify redundant information and generate more concise summaries by combining semantically similar sentences.

Common Models for Sentence Similarity

Some of the most widely used models for sentence similarity tasks include:

  • Sentence-BERT (SBERT): A variant of BERT fine-tuned specifically for sentence-pair tasks, which makes it highly effective for sentence similarity.
  • Universal Sentence Encoder (USE): Developed by Google, USE provides general-purpose sentence embeddings that work well across multiple tasks, including sentence similarity.
  • InferSent: A sentence embedding model that captures sentence-level semantics by using bi-directional LSTM networks.
  • RoBERTa: A robustly optimized variant of BERT, it has proven effective for various NLP tasks, including sentence similarity.

Conclusion

Sentence similarity is a critical NLP task in AI that allows models to understand and measure how closely two sentences relate in terms of meaning. Using both traditional and AI-based approaches, this task has significant applications in search, question answering, plagiarism detection, and much more. With modern advancements like transformer models, the ability to capture sentence-level semantics has become more accurate and efficient.

How to setup a Sentence Similarity LLM on Ubuntu Linux

If you are ready to setup your first Sentence Similarity system follow the instructions in our next page:

How to setup a Sentence Similarity system

Image sources

Figure 1: https://miro.medium.com/v2/resize:fit:720/format:webp/1*IXyYDnBmdEbFvRvO3iZEUQ.png

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