Sentence examples for sensitive matching from inspiring English sources

The dictionary-based named entity recognition was performed with case sensitive matching.

As it uses sensitive matching measures, the proposed algorithm promises good improvement to existing algorithms.

The results did not show a large difference in most of the cases although (partial) case sensitive matching improved the F-score of ChEBI by 7.1 percentage points and reduced the score of TTD by 2.7 percentage points.

To study the influence of case sensitivity on the dictionary-based approach, we indexed the training data using case insensitive, case sensitive, and partial case sensitive matching for all terminological resources (Table 5).

The former are put in a gazetteer list for case insensitive matching of longer names to increase recall, and the latter are used for case sensitive matching of shorter names to increase precision.

However, the linkage methodology used in this study –despite taking into account frequent spelling errors in names– could be less sensitive when matching foreign names and surnames due to a higher inaccuracy, in addition to a higher tendency of foreign citizens to migrate.

Given that cortical regions involved in the generation of the auditory P2 component are sensitive to matching auditory and visual stimuli, the attenuation of P2 may reflect competition between neurons in a multisensory population responsive to different modalities, with competition increasing given irreconcilable incongruence.

The dictionaries performed best with case-sensitive matching but the differences with partial case-sensitive and with case-insensitive matching were marginal.

The effect of different types of case-sensitive matching, use of NP chunking, and removal of dictionary terms that were likely to be highly ambiguous or non-chemical, did not essentially change the performance.

Anrep introduces a two-step approach: first it searches for simple motifs by a threshold-sensitive motif matching algorithm and then it finds the structured motif by an optimized backtracking matching algorithm.

Interestingly, DPP4 serum levels were higher in insulin resistant as compared to insulin sensitive subjects matched for BMI.