Named Entity Recognition with Conditional Random Fields on Turkish News Dataset: Revisiting the Features

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 technique Conditional Random Fields for the named entity recognition in Turkish news texts has been reviewed and the effect of new attributes on the model accuracy has been analyzed. The results of the experiments on different data sets are compared with the similar studies and presented.


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...
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...
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 ...
METU MMDS An Intelligent Multimedia Database System for Multimodal Content Extraction and Querying
Yazıcı, Adnan; Yılmaz, Turgay; Gulen, Elvan; Koyuncu, Murat; Sert, Mustafa (2016-01-04)
Managing a large volume of multimedia data, which contain various modalities (visual, audio, and text), reveals the need for a specialized multimedia database system (MMDS) to efficiently model, process, store and retrieve video shots based on their semantic content. This demo introduces METU-MMDS, an intelligent MMDS which employs both machine learning and database techniques. The system extracts semantic content automatically by using visual, audio and textual data, stores the extracted content in an appr...
Citation Formats
R. F. Çekinel and P. Karagöz, “Named Entity Recognition with Conditional Random Fields on Turkish News Dataset: Revisiting the Features,” 2019, Accessed: 00, 2020. [Online]. Available: