Name
Abstract | ||
|---|---|---|
Given the occurrence frequency of any term within any set of articles within MEDLINE, we define. characteristic. terms as words and phrases that occur in that literature more frequently than expected by chance (at p < 0.001 or better). In this report, we studied how the cut-off criterion varied as a function of literature size and term frequency in MEDLINE as a whole, and have compared the distribution of characteristic terms within a number of journal-defined, affiliation-defined and random literatures. We also investigated how the characteristic terms were distributed among MEDLINE titles, abstracts, and last sentence of abstracts, including. regularized. terms that appear both in the title and abstract of the same paper for at least one paper in the literature. For a set of 10 disciplinary journals, the characteristic terms comprised 18% of the total terms on average. Characteristic terms are utilized in several of our web-based services (Anne O'Tate and Arrowsmith), and should be useful for a variety of other information-processing tasks designed to improve text mining in MEDLINE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.3390/info2020266 | INFORMATION |
Keywords | Field | DocType |
information retrieval, term occurrence, text mining, annotation, literature based discovery | Data mining,Text mining,Annotation,Information retrieval,Computer science,Discipline,Literature-based discovery,MEDLINE,Sentence | Journal |
Volume | Issue | ISSN |
2 | 2 | 2078-2489 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neil R. Smalheiser | 1 | 658 | 57.50 |
| Wei Zhou | 2 | 0 | 0.34 |
| Vetle I. Torvik | 3 | 430 | 27.15 |