Name
Abstract | ||
|---|---|---|
An important search task in the biomedical domain is to find medical records of patients who are qualified for a clinical trial. One commonly used approach is to apply NLP tools to map terms from queries and documents to concepts and then compute the relevance scores based on the concept-based representation. However, the mapping results are not perfect, and none of previous work studied how to deal with them in the retrieval process. In this paper, we focus on addressing the limitations caused by the imperfect mapping results and study how to further improve the retrieval performance of the concept-based ranking methods. In particular, we apply axiomatic approaches and propose two weighting regularization methods that adjust the weighting based on the relations among the concepts. Experimental results show that the proposed methods are effective to improve the retrieval performance, and their performances are comparable to other top-performing systems in the TREC Medical Records Track. |
Year | Venue | Field |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | Weighting,Imperfect,Information retrieval,Ranking,Axiom,Computer science,Regularization (mathematics),Artificial intelligence,Medical record,Machine learning |
DocType | Volume | Citations |
Conference | P14-1 | 8 |
PageRank | References | Authors |
0.61 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajun Wang | 1 | 3185 | 163.17 |
| Xitong Liu | 2 | 89 | 7.81 |
| Hui Fang | 3 | 918 | 63.03 |