Sentence examples for split the document from inspiring English sources
You just split the document into pieces, encrypt those pieces and then pull the document back together on your device.
After the algorithms described above split the document's text into sections and sentences, each sentence is passed to the NLP module with context information including the document type and the section descriptor.
So, the first step is to split the documents into a list of separate term via tokenization and part of speech analysis.
In order to extract the MCSUs, we first need to split the documents into sentences.
Our approach consists of splitting the documents into equal-sized segments.
Splitting the documents into segments and then classifying them has a near-magical effect on precision.
Splitting the documents into 10 subsets required for 10-fold cross validation would have created an uneven distribution of the annotated examples and, while some of the sets would have too many annotated gold standard examples, some sets would have included too few.
Some urged them to "resist the occupation" by rejecting a document that would split the country and sow sectarian strife.
This split the coalition.
