Combined relational and BERT-ranking for multilingual ad hoc document retrieval

With increasing amounts of information available online on the web, it’s crucial for search engines to filter out the content they think is useful and rank that content in decreasing order of relevance to the user’s query so that the user can just focus on the top results. Traditional techniques in search ranking focused on presence of the user’s search terms in the documents being returned by the search engine. Now, modern advances in machine learning allow us to understand complex relationships between what the user really is looking for and what the documents really are about and this research will use this understanding to make better search engine rankings. This research also leverages these advances in technology to also understand the similarities between documents for return meaningful results to user search queries in multiple languages.

Faculty Supervisor:

Gerald Penn

Student:

Nayantara Prem

Partner:

Tealbook inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto