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 st
使用 BERT 嵌入 BiLSTM,发现将整个句子表示策略性地集成到每个单元格的句子表示中,可显著提高序列标注任务的 F1 得分和准确性。在包含 9 个数据集的序列标注任务中,涵盖了命名实体识别(NER)、词性标注和端到端基于方面的情感分析(E2E-ABSA),所有数据集的 F1 得分和准确率都有显著提高。