命名实体和形态学的神经建模（NEMO ^2）
Neural Modeling for Named Entities and Morphology (NEMO^2)
摘要：
命名实体识别（NER）是一项基本的NLP任务，通常被表述为对一系列标记的分类。形态丰富语言（Morphologically-Rich Languages，MRL）对这一基本公式提出了挑战，因为命名实体的边界不一定与标记边界一致，而是尊重形态边界。为了解决MRL中的NER，我们需要回答两个基本问题，即要标记的基本单位是什么，以及如何在现实环境中检测和分类这些单位，即，其中没有可用的金形态。我们实证调查这些问题上一个新的NER基准，并行tokenlevel和语素级NER注释，我们开发的现代希伯来语，形态丰富和模糊的语言。我们的研究结果表明，明确建模形态边界导致提高NER性能，一种新的混合架构，其中NER之前和修剪形态分解，大大优于标准的管道，其中形态分解严格先于NER，设置一个新的性能酒吧希伯来语NER和希伯来语形态分解任务。
英文摘要：
Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically-Rich Languages (MRLs) pose a challenge to this basic formulation, as the boundaries of Named Entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings, i.e., where no gold morphology is available. We empirically investigate these questions on a novel NER benchmark, with parallel tokenlevel and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.
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。
