End-to-End Query Term Weighting (TW-BERT)
Topics: AI (Deep Learning), Marc Najork, Ranking, Scoring, Search Query Processing
The document discusses a new model called Term Weighting BERT (TW-BERT) aimed at improving the effectiveness of lexical retrieval systems by predicting weights for query terms such as unigrams and bigrams. This approach enables a more targeted and effective retrieval by integrating directly with the existing scoring functions like BM25, reducing the integration complexity typically associated with deep learning methods in real-world search applications.
BM25 is a popular ranking function used in information retrieval systems to estimate the relevance of documents to a given search query. It belongs to a family of scoring functions known as probabilistic information retrieval models, which are based on the probabilistic relevance framework. More on BM25.
Your advantages:
+ Full analysis of hundreds of well researched active Microsoft and Google patents and research paper.
+ Save a lot of time and get insights in just a few minutes, without having to spend hours analyzing the documents.
+ Get quick exclusive insights about how search engines and Google could work with easy to understand summaries and analysis.
+ All patents classified by topic for targeted research.
+ New patent summaries every month. Notification via E-Mail
+ Use the AI Research Tools to gain insights from all documents in the database, the Google API Leak, Antitrust trial documents, the whole google support documents and more in seconds
+ Gain fundamental insights for your SEO work and become a real thought leader.
Get access to the SEO Research Suite and become a SEO thought leader now!
