Semantic Search

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Semantic search is a type of search that tries to understand the meaning of the search query and the content of the documents being searched, in order to return the most relevant results. This is in contrast to lexical search, which simply matches keywords in the query to keywords in the documents.

Semantic search is able to achieve better results than lexical search by using a variety of techniques, including:

  • Natural Language Processing (NLP): NLP techniques can be used to extract the meaning from the search query and the documents being searched.
  • Text embeddings: Text embeddings are a way of representing text in a numerical format. This allows semantic search algorithms to compare the meaning of different pieces of text, even if they use different words.

One way to think about the difference between semantic search and lexical search is to imagine that you are looking for information about how to make a cake. With lexical search, you would enter the keywords "make cake" into the search engine. The search engine would then return all of the documents that contain those keywords. This might include documents about making different types of cakes, as well as documents about other topics, such as cake decorating or cake recipes.

With semantic search, the search engine would use NLP techniques to understand that you are looking for information about how to bake a cake. It would then use text embeddings to compare the meaning of the search query to the meaning of the documents in its index. This would allow the search engine to return the most relevant documents, such as recipes for different types of cakes or instructions on how to bake a cake.

Text embeddings are an essential part of semantic search. They allow semantic search algorithms to compare the meaning of different pieces of text, even if they use different words. This is because text embeddings are trained on a large corpus of text, and they learn to represent similar pieces of text in a similar way.

For example, the text embeddings for the words "cake" and "dessert" would be very similar, because these words are semantically related. This means that a semantic search algorithm would be able to identify documents that are relevant to the search query "cake", even if they do not contain the keyword "dessert".

Limitations

Embeddings and similarity are powerful tools for semantic search, but they have some limitations.

Limitations of embeddings

  • Embeddings can be biased. Embeddings are trained on a corpus of text, which may reflect the biases of the authors of that text. This means that embeddings may learn to represent certain words or concepts in a more positive or negative light than others.
  • Embeddings cannot capture all aspects of meaning. Embeddings are a numerical representation of text, and they cannot capture all of the nuances of human language. For example, embeddings may not be able to capture the difference between the different meanings of a word, such as the word "bank" (as in a financial institution) or "bank" (as in the side of a river).
  • Embeddings can be computationally expensive. Training and using embeddings can be computationally expensive, especially for large datasets.

Limitations of similarity

  • Similarity metrics can be inaccurate. Similarity metrics are used to compare the meaning of different pieces of text. However, these metrics can be inaccurate, especially for text that is complex or ambiguous.
  • Similarity metrics may not be able to capture all aspects of semantic similarity. Semantic similarity is a complex concept, and similarity metrics may not be able to capture all of its aspects. For example, two pieces of text may be semantically similar even if they do not use the same words or have the same structure.

Despite these limitations, embeddings and similarity are still powerful tools for semantic search. By using these techniques, semantic search algorithms can achieve better results than traditional lexical search algorithms.

Here are some examples of how the limitations of embeddings and similarity can impact semantic search:

  • A semantic search engine that uses embeddings that are biased against certain groups of people may return less relevant results for those groups.
  • A semantic search engine that uses a similarity metric that is inaccurate for complex or ambiguous text may return irrelevant results for those types of queries.
  • A semantic search engine that uses a similarity metric that cannot capture all aspects of semantic similarity may miss some relevant results.

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