Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Named entity recognition experiments on Turkish texts
Date
2009-10-28
Author
Küçük, Dilek
Yazıcı, Adnan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
176
views
0
downloads
Cite This
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 punctuation since they may be missing in texts obtained from the Web or the output of automatic speech recognition tools. The evaluation of the system is performed on news texts along with other genres encompassing child stories and historical texts, but as expected in case of manually engineered rule-based systems, it suffers from performance degradation on these latter genres of texts since they are distinct from the target domain of news texts. Furthermore, the system is evaluated on transcriptions of news videos leading to satisfactory results which is an important step towards the employment of NER during automatic semantic an notation of videos in Turkish. The current study is significant for its being the first rule-based approach to the NER task on Turkish texts with its evaluation on diverse text types.
Subject Keywords
Information extraction
,
Named entity recognition
,
Turkish
URI
https://hdl.handle.net/11511/54968
Conference Name
8th International Conference on Flexible Query Answering Systems
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
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...
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...
The CHEMDNER corpus of chemicals and drugs and its annotation principles
Krallinger, Martin; et. al. (2015-01-19)
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 ...
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...
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...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
D. Küçük and A. Yazıcı, “Named entity recognition experiments on Turkish texts,” Roskilde Univ, Dept Commun, Business & Informat Technol, Roskilde, DENMARK, 2009, vol. 5822, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54968.
