Sentence examples for document we use from inspiring English sources

Ben-Cohen explains: "The document we use every day is not our passport, but our CV.

As a background corpus for each document we use a collection of public domain novels, providing a generic model that allows topic-relevant terminology to emerge.

To characterize the emotion content of a document we use the Affective Norms for English Words (ANEW) lexicon, which consists of 1034 words that were assigned numerical scores with respect to three emotional "axes" – happiness, arousal, and dominance – by human subjects [45].

In this review we use the term care pathway as a general term, except when another term has been used in a document we use the original term.

In this document, we use 'NIPT' as a general term for non-invasive prenatal testing based on quantitative or qualitative analysis of cfDNA in maternal blood, used in the context of prenatal screening.

Throughout this document we use ISQua's definition for accreditation: "A public recognition by a healthcare accreditation body of the achievement of accreditation standards by a health care organization, demonstrated through an independent external peer assessment of that organization's level of performance in relation to the standards" [ 9].

In order to make sure that the NER tools developed on the CHEMDNER corpus will generalize well on any chemistry-related document we used a careful selection strategy.

Because information regarding a gene gene interaction may be spread out across multiple sentences in a document, we used conditional random fields to link candidate relations to the previous mention of the gene pair in the document.

In Supplementary Materials document, we used two different data types as examples (see examples 1 and 2 in Supplementary Materials for the detailed operation flows) to present the metan command for the meta-analysis of prognostic test accuracy.

T o specify the database image of documents, we use structuring schemas that consist in grammars annotated with database programs.

While learning parameters from documents, we use the output y t of x t as the prediction the next word x t + 1, and the object function is given in Eq. 4.