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Google Ranking Algorithm Research Introduces TW-BERT

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TW-BERT, described in a Google white paper, is a unique framework that boosts search rankings with minimal effort.

Highlights

TW-BERT is an end-to-end query term weighting framework that bridges two paradigms to improve search results

  • Integrates with existing query expansion models and improves performance
  • Deploying the new framework requires minimal changes

    Term Weighting BERT (TWBERT) is a unique ranking framework that Google has announced. It enhances search results and can be easily integrated into preexisting ranking systems.

This new architecture is a breakthrough that enhances ranking procedures generally, including query expansion, though Google has not stated that it is utilising it.
¬† ¬† ¬†¬†It’s¬†also¬†quite¬†simple¬†to¬†implement,¬†which¬†increases¬†the¬†likelihood¬†that¬†it¬†will¬†be¬†used.

TW-BERT has many co-authors, among them is Marc Najork, a Distinguished Research Scientist at Google DeepMind and a former Senior Director of Research Engineering at Google Research.

      He has co-authored many research papers on topics of related to ranking processes, and many other fields.

What is TW-BERT?

TW-BERT is a ranking framework that assigns scores (called weights) to words within a search query in order to more accurately determine what documents are relevant for that search query.

Query Expansion is another area where TW-BERT shines.

By rephrasing or expanding a search query (for as by adding the term “recipe” to the query “chicken soup”), a better match can be made between the search query and the content.

Bringing Together Two Information Retrieval Models with TW-BERT

The report presents a comparison of two search strategies. Both statistical and deep learning models are considered.

The advantages and disadvantages of each technique are then discussed, with the suggestion that TW-BERT can serve as a middle ground that avoids the drawbacks of both.

TW-BERT Bridges Two Approaches

Standard Lexical Retrieval

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Term Weighted Retrieval (TW-BERT)

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TW-BERT is Easy to Deploy

TW-BERT’s ease of integration into preexisting information retrieval ranking processes is one of its many benefits.

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