IBM Watson on Jeopardy! (2011) › Watson's Architecture

Watson used a parallel pipeline to analyze clues, retrieve evidence, generate candidate answers, and score them. Thousands of processor cores allowed many candidate-generation and evidence-analysis methods to run at once.

The pipeline contained specialized components. Named entity recognition identified people, places, organizations, dates, and other entities. Relation extraction proposed connections among entities, while parsing and question classification described the clue's structure and expected answer type. Their outputs fed candidate generation and evidence scoring.

Watson generated dozens or hundreds of candidate answers through different search and analysis methods rather than committing to one search path. It then gathered evidence for each candidate from several sources.

Supervised models ranked candidates from features describing supporting evidence, source reliability, clue-answer match quality, and patterns learned from historical Jeopardy! data. The resulting score estimated whether a candidate was likely enough to justify buzzing.

Generating multiple hypotheses let Watson retain competing interpretations until the scoring stage. It could then select the candidate with the strongest combined evidence rather than relying on the first plausible match.