Leveraging NLP and Data Labeling for More Relevant Search

Search engines have become essential tools, but their results are not always relevant. This can be frustrating for users and costly for businesses.
This video discusses how two technologies can improve search relevance:
1) Natural Language Processing (NLP) - NLP models can analyze search queries to better understand user intent beyond just the keywords. This helps return more contextually relevant results.
2) Data Labeling - Labeling data to "train" search algorithms helps them recognize nuances and variations in language to improve accuracy.
The combination of NLP and data labeling can significantly enhance search relevance. NLP enables a deeper understanding of queries while data labeling exposes search algorithms to a wider range of examples to learn from.