论文标题
对土耳其命名实体识别的最新神经序列标记模型的评估
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition
论文作者
论文摘要
命名实体识别（NER）是一个广泛研究的任务，可以在文本中提取和分类命名实体。 NER不仅在下游语言处理应用程序中至关重要，例如提取关系和问答，而且在大规模的大数据操作中，例如对在线数字媒体内容的实时分析。最近对土耳其语的研究工作是一种较少的具有形态丰富性质的语言，它证明了神经体系结构对良好的文本的有效性，并通过将任务作为序列标记问题提出，从而产生了最先进的结果。在这项工作中，我们凭经验研究了在同一环境中提出的针对土耳其NER标签的最新神经体系结构（双向长期记忆和基于变压器的网络）的使用。我们的结果表明，可以建模远程上下文的基于变压器的网络克服了Bilstm网络的局限性，在该字符，子字和单词级别上使用不同的输入功能。我们还提出了一个基于有条件的随机字段（CRF）层的基于变压器的网络，该网络导致公共数据集上的最新结果（95.95 \％f-measure）。我们的研究有助于量化转移学习对处理形态丰富语言的影响的文献。
Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95\% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.
