  The sentence is a fundamental unit in many NLP applications. Sentence
segmentation is widely used as the first preprocessing task, where an input
text is split into consecutive sentences considering the end of the sentence
(EOS) as their boundaries. This task formulation relies on a strong assumption
that the input text consists only of sentences, or what we call the sentential
units (SUs). However, real-world texts often contain non-sentential units
(NSUs) such as metadata, sentence fragments, nonlinguistic markers, etc. which
are unreasonable or undesirable to be treated as a part of an SU. To tackle
this issue, we formulate a novel task of sentence identification, where the
goal is to identify SUs while excluding NSUs in a given text. To conduct
sentence identification, we propose a simple yet effective method which
combines the beginning of the sentence (BOS) and EOS labels to determine the
most probable SUs and NSUs based on dynamic programming. To evaluate this task,
we design an automatic, language-independent procedure to convert the Universal
Dependencies corpora into sentence identification benchmarks. Finally, our
experiments on the sentence identification task demonstrate that our proposed
method generally outperforms sentence segmentation baselines which only utilize
EOS labels.
