Impedance matching of humans ⇔ machines in high-Q information retrieval systems

Treating the information retrieval (IR) task as one of classification has been shown to be the most effective way to achieve high performance. In real-world Systems, a human is the ultimate determinant of relevance and must be integrated symbiotically into the control structures. We report on a hybrid, Human-Assisted Computer Classification system that opportunistically pairs processes of Active Learning and User Modeling to produce a high-Q computational engine. Top-down human goals are impedance-matched with bottom-up corpus analysis utilizing critical control loops. The System contributions of humans and machines as ´Proxy,´ ´Assessor,´ and ´Classifier´ elements are blended through inter-related ´Model,´ ´Match,´ and ´Measure´ processes (M³) to achieve consistently high precision IR with high recall. We report results for over a dozen topics, with confirmation of internal measures from topic 103 of the 2008 TREC legal track´s interactive task.