Name
Abstract | ||
|---|---|---|
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 knowl- edge 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 effi- cacy of Bayesian Inferencing on lexical relations are soft word sense disambiguation , parameter smooth- ing which ameliorates the data sparsity problem and estimation of joint probability over words which overcomes the deficiency of naive-bayes-like ap- proaches. |
Year | Venue | Keywords |
|---|---|---|
2003 | TREC | question answering,bayesian inference,naive bayes |
Field | DocType | Citations |
Ontology (information science),Data mining,Question answering,Joint probability distribution,Bayesian inference,Computer science,Smoothing,Natural language processing,Artificial intelligence,Bayesian statistics,Bayesian probability,Polysemy | Conference | 6 |
PageRank | References | Authors |
0.79 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deepa Paranjpe | 1 | 160 | 9.39 |
| Ganesh Ramakrishnan | 2 | 521 | 59.32 |
| Sumana Srinivasan | 3 | 32 | 2.90 |