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Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of...
There is an increasing number of community-based question and answer (cQA) service on the Web. Many tasks in cQA services involve in determining similarity between questions. However, finding similar questions is not trivial. In this paper we propose a new method based on a query expansion technique to tackle the similar question matching problem. We employ the information provided by the corresponding...
In this paper, we present a context-sensitive approach for re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional context is learnt from the top N and the last N' documents in initial retrieval ranked list, which contains lexical context and conceptual context. The probabilities that retrieved...
At present, using focused crawler becomes a way to seek the needed information. The main characteristic of a focused web crawler is to select and retrieve only relevant web pages in each crawling process. In this paper, we propose a learnable algorithm that combines link analysis with web content in order to retrieve specific web documents, and it can predict the next URL through learning. The algorithm...
Previous research has shown that using term relationships within language model could improve the effectiveness of information retrieval. But all models only consider adjacent relation or distant relation, none of them combine the two types relation. So in this paper, we propose a new dependency language model for improving information retrieval. In our model, phrases and co-occurrence terms are integrated...
One of the limitations with the current relationship-based IR models is that a relation is often recorded as a binary form, such as R(Term1,Term2), which is only composed of general information of a pair of two terms which are semantically and syntactically related to each other. To tackle this problem, a triple is defined in this paper as a data structure for the integration of a pair of concepts...
Huge biomedical literatures result in many new challenges on text classification, its efficiency and sparseness of data attract many researchers. Recent success of language modeling in information retrieval have let us consider again about multinomial Naive Bayes for text classification. In this paper, we propose a semantic smoothing method for Naive Bayes model, biomedical documents were indexed...
Recent work has shown that ontology is useful to improve the performance of information retrieval, especially in biomedical literatures. The method of ontology-based can solve synonym problems. In this paper, we propose a new frame for genomic information retrieval based on UMLS. In our frame, genomic information retrieval includes three processes: first, documents were indexed based UMLS, which means...
Accurately estimating language model is important to improve the performance of information retrieval. The key problems include solving synonymy and polysemy problem, and smoothing the seen term or not seen term in a document. In this paper, we propose a new method for topic language model. First, concept-based clustering is performed using improved fuzzy c-means. The clustering result is considered...
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