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In this paper, we propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using Wikipedia to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, we further evaluate our approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that our approach can achieve promising performance improvements.

In this paper, we present a context-sensitive approach to re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional positive context is learnt from the top N retrieved documents and a group of negative contexts are learnt from the last N′ documents in initial retrieval ranked list. The contextual space contains lexical context and conceptual context. The probabilities that retrieved documents are generated within the contextual space are then computed for document re-ranking. Empirical evaluation on the TREC Genomics full-text collection and three high-performance biomedical literature retrieval runs demonstrates that the context-sensitive re-ranking approach yields better retrieval performance.

Background In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. Results In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. Conclusions The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.

In this paper, we propose an approach based on a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. Experiments on TREC 2007 Genomics collection and two distinctive IR baseline runs demonstrate the effectiveness of our method in promoting ranking diversity for biomedical information retrieval. Evaluation results show that our approach can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track.

Background In the biomedical domain, the desired information of a question (query) asked by biologists usually is a list of a certain type of entities covering different aspects that are related to the question, such as genes, proteins, diseases, mutations, etc. Hence it is important for a biomedical information retrieval system to be able to provide comprehensive and diverse answers to fulfill biologists’ information needs. However, traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. This assumption may result in high redundancy and low diversity in the retrieval ranked lists. Results In this paper, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. It consists of two component models, namely the aspect-term relevance model and the aspect-term novelty model. They model the relevance of a document and the novelty of a document respectively. We show that our approach can achieve 16. 4% improvement over the highest aspect level MAP reported in the TREC 2007 Genomics track, and 9. 8% improvement over the highest passage level MAP reported in the TREC 2007 Genomics track. Conclusions The proposed combination model which models aspects, terms, topic relevance and document novelty as potential functions is demonstrated to be effective in promoting ranking diversity as well as in improving relevance of ranked lists for genomics search. We also show that the use of aspect plays an important role in the model. Moreover, the proposed model can integrate various different relevance and novelty measures easily.