– Paper discusses data structure for quick lookup of documents with specific words.
– Inverted index assigns ID and maps terms to document list.
– Requires removing common words and formalizing language rules for keyword search.
– Neural models can learn documents without language expertise.
– Vector search is a significant advancement compared to traditional keyword search.
– Vector search is better at assessing semantic similarity.
– Vector search is scalable and has lower maintenance.
– Vector search is slower but can be sped up with NeuralHashing.
– Vector search has complex explainability.
– Paper also mentions specific search results for mobile phone models.
– Requires formalizing language, segment, and case rules for keyword search
– Data structure allows quick lookup of documents containing specific words
– Vector search is better at assessing semantic similarity
– Vector search is slower but can be sped up with NeuralHashing
– Vector search has complex explainability
– Traditional keyword search is lower maintenance and scalable
– Vector search is best for unique and rare queries
– Requires formalizing language, segment, and case rules for keyword search
– Data structure allows quick lookup of documents containing specific words
– Vector search is a significant advancement compared to traditional keyword search
– Vector search is better at assessing semantic similarity
– Vector search is scalable and has lower maintenance
– Vector search can handle unique and rare queries effectively
– Vector search has higher cost and slower speed compared to keyword search
– Vector search lacks complex explainability features like highlighting relevant excerpts
– Vector search is a significant advancement in search capabilities.
– Vector search is better at assessing semantic similarity.
– Vector search is scalable and has lower maintenance.
– Traditional keyword search is slower and has higher cost.
– Vector search is complex in terms of explainability.
– Vector search is a new way to find information online.
– It is better at understanding the meaning of words.
– It can quickly find documents that contain specific words.
– It is more advanced than traditional keyword search.
– It can handle unique and rare queries better.
– It may be more expensive and slower than keyword search.
– It can be complex to explain how it works.
