Named Entity Recognition in Turkish with Bayesian Learning and Hybrid Approaches

RehaYavuz, Sermet
Kucuk, Dilek
Yazıcı, Adnan
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 performance rates. This paper is significant as it reports the first use of the Bayesian approach for the task of named entity recognition on Turkish texts for which especially practical approaches are still insufficient.


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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...
A hybrid named entity recognizer for Turkish
Kucuk, Dilek; Yazıcı, Adnan (2012-02-15)
Named entity recognition is an important subfield of the broader research area of information extraction from textual data. Yet, named entity recognition research conducted on Turkish texts is still rare as compared to related research carried out on other languages such as English, Spanish, Chinese, and Japanese. In this study, we present a hybrid named entity recognizer for Turkish, which is based on a manually engineered rule based recognizer that we have proposed. Since rule based systems for specific d...
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...
Person name recognition in turkish financial texts by using local grammar approach
Bayraktar, Özkan; Taşkaya Temizel, Tuğba; Department of Information Systems (2007)
Named entity recognition (NER) is the task of identifying the named entities (NEs) in the texts and classifying them into semantic categories such as person, organization, and place names and time, date, monetary, and percent expressions. NER has two principal aims: identification of NEs and classification of them into semantic categories. The local grammar (LG) approach has recently been shown to be superior to other NER techniques such as the probabilistic approach, the symbolic approach, and the hybrid a...
Citation Formats
S. RehaYavuz, D. Kucuk, and A. Yazıcı, “Named Entity Recognition in Turkish with Bayesian Learning and Hybrid Approaches,” 2013, vol. 264, Accessed: 00, 2020. [Online]. Available: