title: A LDA-based approach to promoting ranking diversity for genomics information retrieval.
abstract: 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.
authors: State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
authors: Yan
authors: Y
authors: Chen
authors: Xiaoshi
authors: X
authors: Yin
authors: Zhoujun
authors: Z
authors: Li
authors: Xiaohua
authors: X
authors: Hu
authors: Jimmy Xiangji
authors: JX
authors: Huang
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000379:methods; D023281:Genomics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008957:Models, Genetic; D015203:Reproducibility of Results