  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.
