Name
Abstract | ||
|---|---|---|
Based on our findings, yes, MTI is still relevant and useful, and needs to be improved and expanded. The BioASQ Challenge results have shown that we need to incorporate more machine learning into MTI while still retaining the indexing rules that have earned MTI the indexers' trust over the years. We also need to expand MTI through the use of full text, when and where it is available, to provide coverage of indexing terms that are typically only found in the full text. The role of MTI at NLM is also expanding into new areas, further reinforcing the idea that MTI is increasingly useful and relevant. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s13326-017-0113-5 | J. Biomedical Semantics |
Keywords | Field | DocType |
BioASQ,Indexing methods,MEDLINE,Machine learning,MeSH,Text categorization | Data science,Data mining,Information retrieval,Ranking,Workload,Indexer,Computer science,Search engine indexing,Text categorization,MEDLINE | Journal |
Volume | Issue | ISSN |
8 | 1 | 2041-1480 |
Citations | PageRank | References |
5 | 0.43 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James G. Mork | 1 | 647 | 65.22 |
| Alan R. Aronson | 2 | 2551 | 260.67 |
| Dina Demner Fushman | 3 | 1717 | 147.70 |