TNT-KID：用于关键词识别的基于转换器的神经标签器
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
摘要：
随着可用文本数据量的增加，开发能够自动分析、分类和汇总这些数据的算法已成为必要。在这项研究中，我们提出了一种新的算法的关键字识别，即，提取代表给定文档关键方面的一个或多个单词短语，称为基于转换器的关键词识别神经标记器（TNT-KID）。通过使Transformer架构适应手头的特定任务并利用领域特定语料库上的语言模型预训练，该模型能够通过在各种不同的数据集上提供有竞争力和鲁棒的性能，同时仅需要最佳性能系统所需的一小部分手动标记数据，来克服关键字提取的有监督和无监督的最先进方法的不足。这项研究还提供了彻底的错误分析，对模型的内部工作有价值的见解，以及测量关键字识别工作流程的特定组件对整体性能的影响的消融研究。
英文摘要：
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity. In this research we present a novel algorithm for keyword identification, i.e., an extraction of one or multi-word phrases representing key aspects of a given document, called Transformer-based Neural Tagger for Keyword IDentification (TNT-KID). By adapting the transformer architecture for a specific task at hand and leveraging language model pretraining on a domain specific corpus, the model is capable of overcoming deficiencies of both supervised and unsupervised state-of-the-art approaches to keyword extraction by offering competitive and robust performance on a variety of different datasets while requiring only a fraction of manually labeled data required by the best performing systems. This study also offers thorough error analysis with valuable insights into the inner workings of the model and an ablation study measuring the influence of specific components of the keyword identification workflow on the overall performance.
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。
