Name
Abstract | ||
|---|---|---|
The problem of re-ranking initial retrieval re- sults exploring the intrinsic structure of docu- ments is widely researched in information re- trieval (IR) and has attracted a considerable amount of time and study. However, one of the drawbacks is that those algorithms treat queries and documents separately. Further- more, most of the approaches are predomi- nantly built upon graph-based methods, which may ignore some hidden information among the retrieval set. This paper proposes a novel document re- ranking method based on Latent Dirichlet Al- location (LDA) which exploits the implicit structure of the documents with respect to original queries. Rather than relying on graph- based techniques to identify the internal struc- ture, the approach tries to find the latent struc- ture of "topics" or "concepts" in the initial re- trieval set. Then we compute the distance be- tween queries and initial retrieval results based on latent semantic information deduced. Em- pirical results demonstrate that the method can comfortably achieve significant improvement over various baseline systems. |
Year | DOI | Venue |
|---|---|---|
2009 | null | empirical methods in natural language processing |
Keywords | Field | DocType |
intrinsic structure,information retrieval,internal structure,retrieval set,initial re-trieval set,latent structure,initial retrieval result,hidden information,latent document re-ranking,implicit structure,latent semantic information | Graph,Latent Dirichlet allocation,Information retrieval,Ranking,Computer science,Semantic information,Exploit,Probabilistic latent semantic analysis,Artificial intelligence,Natural language processing,Machine learning,Instrumental and intrinsic value | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
9 | 0.66 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Zhou | 1 | 342 | 25.99 |
| Vincent Wade | 2 | 106 | 14.94 |
| M S Sridhar | 3 | 19 | 2.01 |