Google Search as a Hybrid System of Lexical and Semantic Search
Posted: Wed Feb 19, 2025 6:40 am
Today, Google works lexically and entity-based. Depending on how clearly Google recognizes the connections between entities in the search queries and documents, Google uses semantic or lexical approaches to obtain information in recall and precision.
Semantic search goes beyond simple keyword matching by understanding the complex relationships between words, thereby capturing the meaning or context of the query and documents. This is critical to finding relevant documents that do not contain the exact search terms but are related in context.
The approach is based on the premise that effective semantic models, especially in recent years, have been developed primarily using deep neural networks. These models are able to understand the nuances of language, including synonyms, related terms and context, which are often missed by lexical models.
The semantic retrieval model is based on deep neural networks, which in particular use architectures such as BERT (Bidirectional Encoder Representations from Transformers). BERT and similar models are trained with large amounts of text data to understand complex language patterns and semantics.
For the retrieval process, the model generates embeddings for denmark cell phone number list both queries and documents. These embeddings represent the semantic content of the text in a high-dimensional space in which the semantic similarity between a query and a document can be measured, usually using cosine similarity.
Improved recall: By capturing the meaning behind words, the semantic approach can locate a wider range of relevant documents, including those that do not contain exactly the same keywords as the query. This is particularly useful for solving the problem of vocabulary mismatch, where the query and the relevant documents use different terms to describe the same concept.
You can find out more about semantic retrieval in my overview Most interesting Google Patents for semantic search.
Semantic search goes beyond simple keyword matching by understanding the complex relationships between words, thereby capturing the meaning or context of the query and documents. This is critical to finding relevant documents that do not contain the exact search terms but are related in context.
The approach is based on the premise that effective semantic models, especially in recent years, have been developed primarily using deep neural networks. These models are able to understand the nuances of language, including synonyms, related terms and context, which are often missed by lexical models.
The semantic retrieval model is based on deep neural networks, which in particular use architectures such as BERT (Bidirectional Encoder Representations from Transformers). BERT and similar models are trained with large amounts of text data to understand complex language patterns and semantics.
For the retrieval process, the model generates embeddings for denmark cell phone number list both queries and documents. These embeddings represent the semantic content of the text in a high-dimensional space in which the semantic similarity between a query and a document can be measured, usually using cosine similarity.
Improved recall: By capturing the meaning behind words, the semantic approach can locate a wider range of relevant documents, including those that do not contain exactly the same keywords as the query. This is particularly useful for solving the problem of vocabulary mismatch, where the query and the relevant documents use different terms to describe the same concept.
You can find out more about semantic retrieval in my overview Most interesting Google Patents for semantic search.