SpanNER：命名实体重新识别/识别为跨度预测
SpanNER: Named Entity Re-/Recognition as Span Prediction
摘要：
近年来，命名实体识别（NER）系统从序列标记到跨度预测的范式转变。尽管其初步的有效性，跨度预测模型的建筑偏见尚未得到充分理解。本文首先研究了跨度预测模型用于命名实体识别时与序列标记框架相比的优势和不足，以及如何进一步改进它，这促使我们基于不同范式的系统优势互补。然后，我们揭示了跨度预测，同时，可以作为一个系统组合器，重新识别命名实体从不同的系统的输出。我们在11个数据集上实验实现了154个系统，涵盖了三种语言，综合的结果表明跨度预测模型既可以作为基础NER系统，也可以作为系统组合器。我们提供所有代码和数据集：\url{https://github.com/neulab/spanner}，以及在线系统演示：\url{http://spanner.sh}。我们的模型也已部署到ExplainaBoard平台，它允许用户以交互方式灵活地执行最高评分系统的系统组合：\url{http://explainaboard.nlpedia.ai/leaderboard/task-ner/}。
英文摘要：
Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model's architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the span prediction model is used for named entity recognition compared with the sequence labeling framework and how to further improve it, which motivates us to make complementary advantages of systems based on different paradigms. We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems' outputs. We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners. We make all code and datasets available: \url{https://github.com/neulab/spanner}, as well as an online system demo: \url{http://spanner.sh}. Our model also has been deployed into the ExplainaBoard platform, which allows users to flexibly perform a system combination of top-scoring systems in an interactive way: \url{http://explainaboard.nlpedia.ai/leaderboard/task-ner/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
