Question Answering Via Bayesian Inference On Lexical Relations

Many researchers have used lexical networks and ontologies to mitigate synonymy and polysemy problems in Question Answering (QA), systems coupled with taggers, query classifiers, and answer extractors in complex and ad-hoc ways. We seek to make QA systems reproducible with shared and modest human effort, carefully separating knowledge from algorithms. To this end, we propose an aesthetically “clean” Bayesian inference scheme for exploiting lexical relations for passage-scoring for QA . The factors which contribute to the efficacy of Bayesian Inferencing on lexical relations are soft word sense disambiguation, parameter smoothing which ameliorates the data sparsity problem and estimation of joint probability over words which overcomes the deficiency of naive-bayes-like approaches. Our system is superior to vector-space ranking techniques from IR, and its accuracy approaches that of the top contenders at the TREC QA tasks in recent years.