Sentence examples for matching terms with from inspiring English sources

Obviously this automated technique can't provide the level of accuracy or relevancy that could be achieved by human input, but Asterpix representatives say that the system has been remarkably good at matching terms with the right objects.

For each gene, g ∈ G CNV, a phenomatch score S g is defined based on the information content of all matching terms with specificity above a certain similarity threshold λ to exclude relatively nonspecific phenotypic features: In our analysis, we selected λ to be 2.5, corresponding to a frequency for the feature of 786 among all 9580 analyzed genes.

PolySearch employs a text ranking scheme to score the most relevant sentences and abstracts that associate both the query and match terms with each other.

With the noteworthy exception of the later Nishitani, however, the Kyoto School tends to favor the Chinese glyph mu ("nothingness"; wu in Chinese), which is found predominantly in Zen, and which reflects the early attempt to "match terms" with Daoism in the translation and interpretive development of Buddhism in China.

While one can easily view parts of full papers relevant in the curated articles, the Integrator interprets the annotations in abstracts given by the TM systems and displays them in the same format including article title, authors, publication date, the name of journals and the abstract with the matching terms highlighted.

Terms are tested for matches in descending order of length, so the longest possible matching term is always replaced with the appropriate CTERM or GTERM token.

Several innovative approaches to autocoding have used the higher information content of multiword terms (also called word n-grams) to match terms in text with terms in vocabularies or to enhance the content of vocabularies by identifying n-grams occurring in text that qualify as new nomenclature terms [ 2- 4].

3) Match terms with turns.

But the clear upshot of the Buddhist's nominalist position about universals is that there are no such natural kinds, and that the usage of terms is not explicable by matching up terms with them.

Summarizing, IRT based text analysis alone is unable to solve the problem of the variability of scientific texts written in natural languages because the accuracy of matching thesaurus terms with real text phrases leaves much to be desired.

This prevents matching MeSH terms with overlapping surface forms (e.g. 'Mad hatter disease' with 'Mad cow disease') and makes concept matching as unambiguous as possible.