Difference between revisions of "Semantic Search"
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| − | [https://www.youtube.com/results?search_query= | + | [https://www.youtube.com/results?search_query=ai+Semantic+Search YouTube] |
| − | [https://www.google.com/search?q=Google+Semantic+ | + | [https://www.quora.com/search?q=ai%20Semantic%20Search ... Quora] |
| − | + | [https://www.google.com/search?q=ai+Semantic+Search ...Google search] | |
| + | [https://news.google.com/search?q=ai+Semantic+Search ...Google News] | ||
| + | [https://www.bing.com/news/search?q=ai+Semantic+Search&qft=interval%3d%228%22 ...Bing News] | ||
* [[Papers Search]] | * [[Papers Search]] | ||
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]] | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]] | ||
* [[Google Semantic Reactor]] | * [[Google Semantic Reactor]] | ||
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| + | <u>Semantic search</u> 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 <u>lexical search</u>, which simply matches keywords in the query to keywords in the documents. | ||
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| + | Semantic search is able to achieve better results than lexical search by using a variety of techniques, including: | ||
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| + | * <b>[[Natural Language Processing (NLP)]]</b>: NLP techniques can be used to extract the meaning from the search query and the documents being searched. | ||
| + | * <[[Embedding|Text embeddings]]</b>: 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. | ||
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| + | 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. | ||
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| + | 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. | ||
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| + | 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. | ||
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| + | 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". | ||
Revision as of 10:44, 9 October 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Papers Search
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Google Semantic Reactor
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".
