Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
AZuRE, a Scalable System for Automated Term Disambiguation of Gene and Protein Names
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based biomedical text retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Text similarity: an alternative way to search MEDLINE
Bioinformatics
ADAM: another database of abbreviations in MEDLINE
Bioinformatics
Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Gene symbol disambiguation using knowledge-based profiles
Bioinformatics
Disambiguating biomedical acronyms using EMIM
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Quantifying the impact of concept recognition on biomedical information retrieval
Information Processing and Management: an International Journal
Inferring conceptual relationships to improve medical records search
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Finding biological entities (such as genes or proteins) that satisfy certain conditions from texts is an important and challenging task in biomedical information retrieval and text mining. It is essential for many biomedical applications, such as drug discovery which normally requires collecting existing scientific facts from documents. This paper presents an effective IR system for this task, in which 1) domain knowledge is incorporated to improve retrieval effectiveness; 2) query expansion with related concepts on multiple semantic levels is employed; 3) a gene symbol disambiguation technique is implemented. We evaluated these techniques and examined two different concept-based IR models. Experiments based upon the proposed framework yield significant improvement (22% for automatic and 16.7% for non-automatic) over the best reported results of passage retrieval in the Genomics track of TREC 2007.