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

Cekinel, Recep Firat; Agriman, Mustafa; Karagöz, Pınar; Yilmaz, Burcu

doi:10.1109/siu.2019.8806523

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Arslan et al [18] used Apache Lucene, a big data indexing and search software, to detect the presence of email, web addresses and emoji in half a billion web pages. C xekinel et al [19] recognised named entities in news texts using the CRF method. Akpnar et al [20] proposed a tool that enables semi-automatic tagging of entities and their relationships, based on bank transactions in image format.…”

Section: Introductionmentioning

confidence: 99%

Searchable Turkish OCRed historical newspaper collection 1928–1942

Menhour

Şahin

Sarıkaya

et al. 2021

Journal of Information Science

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The newspaper emerged as a distinct cultural form in early 17th-century Europe. It is bound up with the early modern period of history. Historical newspapers are of utmost importance to nations and its people, and researchers from different disciplines rely on these papers to improve our understanding of the past. In pursuit of satisfying this need, Istanbul University Head Office of Library and Documentation provides access to a big database of scanned historical newspapers. To take it another step further and make the documents more accessible, we need to run optical character recognition (OCR) and named entity recognition (NER) tasks on the whole database and index the results to allow for full-text search mechanism. We design and implement a system encompassing the whole pipeline starting from scrapping the dataset from the original website to providing a graphical user interface to run search queries, and it manages to do that successfully. Proposed system provides to search people, culture and security-related keywords and to visualise them.

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Section: Introductionmentioning

confidence: 99%

Searchable Turkish OCRed historical newspaper collection 1928–1942

Menhour

Şahin

Sarıkaya

et al. 2021

Journal of Information Science

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Named Entity Recognition on Morphologically Rich Language: Exploring the Performance of BERT with varying Training Levels

Kilic

Dinc

Karagöz

2020

2020 IEEE International Conference on Big Data (Big Data)

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Named Entity Recognition in Turkish Bank Documents

Kabasakal¹,

Mutlu²

2021

Kocaeli Journal of Science and Engineering

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Named Entity Recognition (NER) is the process of automatically recognizing entity names such as person, organization, and date in a document. In this study, we focus on bank documents written in Turkish and propose a Conditional Random Fields (CRF) model to extract named entities. The main contribution of this study is twofold: (i) we propose domain-specific features to extract entity names such as law, regulation, and reference which frequently appear in bank documents; and (ii) we contribute to NER research in Turkish document which is not as mature as other languages such as English and German. Experimental results based on 10-fold cross validation conducted on 551 real-life, anonymized bank documents show the proposed CRF-NER model achieves 0.962 micro average F1 score. More specifically, F1 score for the identification of law names is 0.979, regulation name is 0.850, and article no is 0.850.

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

Cited by 3 publications

References 9 publications

Searchable Turkish OCRed historical newspaper collection 1928–1942

Searchable Turkish OCRed historical newspaper collection 1928–1942

Named Entity Recognition on Morphologically Rich Language: Exploring the Performance of BERT with varying Training Levels

Named Entity Recognition in Turkish Bank Documents

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