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Named entity recognition (NER) is an extensively studied task that extractsand classifies named entities in a text. NER is crucial not only in downstreamlanguage processing applications such as relation extraction and questionanswering but also in large scale big data operations such as real-timeanalysis of online digital media content. Recent research efforts on Turkish, aless studied language with morphologically rich nature, have demonstrated theeffectiveness of neural architectures on well-formed texts and yieldedstate-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) proposedfor Turkish NER tagging in the same setting. Our results demonstrate thattransformer-based networks which can model long-range context overcome thelimitations of BiLSTM networks where different input features at the character,subword, and word levels are utilized. We also propose a transformer-basednetwork with a conditional random field (CRF) layer that leads to thestate-of-the-art result (95.95\% f-measure) on a common dataset. Our studycontributes to the literature that quantifies the impact of transfer learningon processing morphologically rich languages.

An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition