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

Sentence Identification with BOS and EOS Label Combinations