Name
Abstract | ||
|---|---|---|
The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to standard bag-of-words based models. A conceptual model has been adopted in general-purpose retrieval which can comprise a range of concepts, including linguistic terms, latent concepts and explicit knowledge concepts. One of the drawbacks of this model is that the computational cost is significant and often intractable in modern test collections. Therefore, approaches utilising concept-based models for re-ranking initial retrieval results have attracted a considerable amount of study. This method enjoys the benefits of reduced document corpora for semantic space construction and improved ranking results. However, fitting such a model to a smaller collection is less meaningful than fitting it into the whole corpus. This paper proposes a dual-space model which incorporates external knowledge to enhance the space produced by the latent concept method. This model is intended to produce global consistency across the semantic space: similar entries are likely to have the same re-ranking scores with respect to the latent and manifest concepts. To illustrate the effectiveness of the proposed method, experiments were conducted using test collections across different languages. The results demonstrate that the method can comfortably achieve improvements in retrieval performance. |
Year | Venue | Keywords |
|---|---|---|
2010 | international conference on computational linguistics | latent concept method,information retrieval,conceptual model,retrieval performance,dual-space model,latent concept,initial retrieval result,concept-based model,general-purpose retrieval,dual-space re-ranking model,document retrieval,dual space,computer science |
Field | DocType | Volume |
Divergence-from-randomness model,Cognitive models of information retrieval,Information retrieval,Ranking,Conceptual model,Computer science,Explicit semantic analysis,Natural language processing,Artificial intelligence,Probabilistic latent semantic analysis,Vector space model,Document retrieval | Conference | C10-2 |
Citations | PageRank | References |
2 | 0.43 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Zhou | 1 | 342 | 25.99 |
| Seamus Lawless | 2 | 155 | 13.27 |
| Jinming Min | 3 | 22 | 4.61 |
| Vincent Wade | 4 | 2 | 0.43 |