Named entity recognition on morphologically rich language: exploring the performance of BERT with varying training levels

Kılıç, Yüksel Pelin
Dinç, Duygu
Karagöz, Pınar


Named Entity Recognition with Conditional Random Fields on Turkish News Dataset: Revisiting the Features
Çekinel, Recep Fırat; Karagöz, Pınar (2019-04-24)
Named entity recognition is a natural language processing problem that aims to mark entity names, such as person, place, organization, date, time, money and percentage, from different types of text. Various applications such as location estimation, event time estimation, determination of important people in the text can be possible with the solutions to this problem. The number of named entity recognition studies on Turkish texts is quite limited compared to those on English. In this study, the use of the t...
Named entity recognition experiments on Turkish texts
Küçük, Dilek; Yazıcı, Adnan (2009-10-28)
Named entity recognition (NER) is one of the main information extraction tasks and research on NER from Turkish texts is known to be rare. In this study, we present a rule-based NER system for Turkish which employs a set of lexical resources and pattern bases for the extraction of named entities including the names of people, locations, organizations together with time/date and money/percentage expressions. The domain of the system is news texts and it does not utilize important clues of capitalization and ...
Named entity recognition in Turkish with bayesian learning and hybrid approaches
Yavuz, Sermet Reha; Yazıcı, Adnan; Küçük, Dilek; Department of Computer Engineering (2011)
Information Extraction (IE) is the process of extracting structured and important pieces of information from a set of unstructured text documents in natural language. The final goal of structured information extraction is to populate a database and reach data effectively. Our study focuses on named entity recognition (NER) which is an important subtask of IE. NER is the task that deals with extraction of named entities like person, location, organization names, temporal expressions (date and time) and numer...
Named Entity Recognition in Turkish with Bayesian Learning and Hybrid Approaches
RehaYavuz, Sermet; Kucuk, Dilek; Yazıcı, Adnan (2013-10-29)
Named entity recognition is one of the significant textual information extraction tasks. In this paper, we present two approaches for named entity recognition on Turkish texts. The first is a Bayesian learning approach which is trained on a considerably limited training set. The second approach comprises two hybrid systems based on joint utilization of this Bayesian learning approach and a previously proposed rule-based named entity recognizer. All of the proposed three approaches achieve promising performa...
Named entity recognition from scratch on social media
Önal, Kezban Dilek; Karagöz, Pınar (null; 2015-09-07)
With the extensive amount of textual data flowing through social media platforms, the interest in Information Extraction (IE) on such textual data has increased. Named Entity Recognition (NER) is one of the basic problems of IE. State-of-the-art solutions for NER face an adaptation problem to informal texts from social media platforms. In this study, we addressed this generalization problem with the NLP from scratch idea that has been shown to be successful for several NLP tasks on formal text. Experimental...
Citation Formats
Y. P. Kılıç, D. Dinç, and P. Karagöz, “Named entity recognition on morphologically rich language: exploring the performance of BERT with varying training levels,” presented at the IEEE International Conference on Big Data (2020), Virtual event, 2020, Accessed: 00, 2021. [Online]. Available: