State of the art in Turkish Named Entity Recognition

Named entity recognition (NER), which provides useful information for many high level NLP applications and semantic web technologies, is a well-studied topic for most of the languages and especially for English. However the studies for Turkish, which is a morphologically richer and lesser-studied language, have fallen behind these for a long while. In recent years, Turkish NER intrigued researchers due to its scarce data resources and the unavailability of high-performing systems. Especially, the need to discover named entities occurring in Web datasets initiated many studies in this field. This article presents the state of the art in Turkish named entity recognition both on well formed texts and user generated content, and introduces the details of the best-performing system so far. The introduced approach uses conditional random fields and obtains the highest results in the literature for Turkish NER with 92% CoNLL score on a dataset collected from Turkish news articles and ∼65% on different datasets collected from Web 2.0. The article additionally introduces the re-annotation of the available datasets to extend the covered named entity types, and a brand new dataset from Web 2.0.