Theme creation for digital collections
DCMI '08 Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications
Using ontology to improve precision of terminology extraction from documents
Expert Systems with Applications: An International Journal
Context-sensitive semantic smoothing using semantically relatable sequences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Granular Computing for Text Mining: New Research Challenges and Opportunities
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Measuring intrinsic quality of semantic search based on feature vectors
International Journal of Metadata, Semantics and Ontologies
Conceptual language models for domain-specific retrieval
Information Processing and Management: an International Journal
International Journal of Web and Grid Services
Summarizing textual information about locations
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Voting techniques for a multi-terminology based biomedical information retrieval
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Wikipedia-based semantic smoothing for the language modeling approach to information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A novel neighborhood based document smoothing model for information retrieval
Information Retrieval
Hi-index | 0.01 |
Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. The previously implemented semantic smoothing models, such as the translation model, have shown good experimental results. However, these models are unable to incorporate contextual information. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document into a set of weighted context-sensitive topic signatures and then translate those topic signatures into query terms. The language model with such a context-sensitive semantic smoothing is referred to as the topic signature language model. In detail, we implement two types of topic signatures depending on whether ontology exists in the application domain. One is the ontology-based concept and the other the multiword phrase. The translation probabilities from each topic signature to individual terms are estimated through the EM algorithm. Document models based on topic signature translation are then derived. The new smoothing method is evaluated on TREC 2004/2005 Genomics Track with ontology-based concepts, and TREC Ad hoc Track (Disk 1, 2 and 3) with multiword phrases. Both experiments show significant improvements over the two-stage language model as well as the language model with context-insensitive semantic smoothing.