Name
Title | ||
|---|---|---|
Automatic indexing of specialized documents: using generic vs. domain-specific document representations |
Abstract | ||
|---|---|---|
The shift from paper to electronic documents has caused the curation of information sources in large electronic databases to become more generalized. In the biomedical domain, continuing efforts aim at refining indexing tools to assist with the update and maintenance of databases such as MEDLINE®. In this paper, we evaluate two statistical methods of producing MeSH® indexing recommendations for the genetics literature, including recommendations involving subheadings, which is a novel application for the methods. We show that a generic representation of the documents yields both better precision and recall. We also find that a domain-specific representation of the documents can contribute to enhancing recall. |
Year | Venue | Keywords |
|---|---|---|
2007 | BioNLP@ACL | biomedical domain,better precision,genetics literature,refining indexing tool,large electronic databases,automatic indexing,domain-specific representation,generic representation,electronic document,domain-specific document representation,indexing recommendation,documents yield,specialized document |
Field | DocType | Citations |
Information retrieval,Computer science,Precision and recall,Search engine indexing,Natural language processing,Artificial intelligence,Recall,Automatic indexing | Conference | 4 |
PageRank | References | Authors |
0.62 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aurélie Névéol | 1 | 565 | 50.50 |
| James G. Mork | 2 | 647 | 65.22 |
| Alan R. Aronson | 3 | 2551 | 260.67 |