Sentence examples for ranking terms from inspiring English sources

DOG4DAG achieves this by ranking terms according to their relative importance (tf-idf).

It would also increase the flexibility of the resulting model: while we currently select the primary annotation terms manually based on their popularity among genes in a given stage range, a simple threshold on the number of genes being annotated, together with an appropriate means of ranking terms, would allow to automatically partition the primary versus latent sets.

In ranking terms, the 19th-seeded Safin's 4-6, 7-5, 6-3, 7-6 (1) loss to Dimitri Tursunov was a surprise.

Direct addition of the ranking terms is then performed to integrate all information into a single decision variable.

Since we are interested in using these profiles for sentence selection, we limit the MeSH term profiles to a few top ranking terms (parameter M).

To further reduce this large space, we experimented with several term selection functions (studied by Yang et al).. Here, we apply the χ criterion (Yang and Pederson, 1997) and select the highest ranking terms.

The unification problem presented by us generalizes some known problems, such as, e.g., order-sorted unification for ranked terms, sequence unification, and word unification with regular constraints.

Such content identification and representation is done by displaying only the selected number of the highest TFiDF ranked terms from a BoW vector constructed for a selected community.

The 20 highest ranked terms appear in Appendix A (included in Supporting Information S1).

There are two top ranked functional terms from GSEA annotations (the No. 1 and No. 2 ranked terms) that are directly related to testis-related gene expression as obviously described by the terms themselves (Table 6).

In addition, the top ranked terms in the GSEA results appeared to have much higher FDR q-values (Table 3, 4) than those identified with SLEPR.