  The paper proposes various strategies for sampling text data when performing
automatic sentence classification for the purpose of detecting missing
bibliographic links. We construct samples based on sentences as semantic units
of the text and add their immediate context which consists of several
neighboring sentences. We examine a number of sampling strategies that differ
in context size and position. The experiment is carried out on the collection
of STEM scientific papers. Including the context of sentences into samples
improves the result of their classification. We automatically determine the
optimal sampling strategy for a given text collection by implementing an
ensemble voting when classifying the same data sampled in different ways.
Sampling strategy taking into account the sentence context with hard voting
procedure leads to the classification accuracy of 98% (F1-score). This method
of detecting missing bibliographic links can be used in recommendation engines
of applied intelligent information systems.
