Turkish Named-Entity Recognition

Yeniterzi, Reyyan; Tür, Gökhan; Oflazer, Kemal

doi:10.1007/978-3-319-90165-7_6

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…Tür et al (2003) employed the last inflectional group in a statistical model that relies on maximum a posteriori estimation for Turkish. In another work, morphemes in a morphological analysis are used as features of a CRF model (Yeniterzi 2011). Şeker and Eryiğit (2012) also employed a CRF model with more extensive features and added gazetteers.…”

Section: Related Workmentioning

confidence: 99%

“…One can argue that the literature already addressed this issue by proposing character-based embeddings in word representations (Lample et al 2016) and entities tagged at the character level (Kuru, Can and Yuret 2016). Moreover, it is possible to note that morphological tags have been employed in the past for the NER task (Tür, Hakkani-Tür and Oflazer 2003; Yeniterzi 2011). However, our work treats the morphological analysis in a number of different ways that can be applied to many MRLs and is the first to propose an embedding-based framework for representing the morphological analysis in the context of NER.…”

Section: Introductionmentioning

confidence: 99%

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The effect of morphology in named entity recognition with sequence tagging

2018

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This work proposes a sequential tagger for named entity recognition in morphologically rich languages. Several schemes for representing the morphological analysis of a word in the context of named entity recognition are examined. Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of morphology in named entity recognition with sequence tagging

2018

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“…Однако качество решения задачи оказалось низким, F-мера 59.0 %. Гораздо лучше показали себя методы, основанные на правилах (rule-based) для турецких текстов, F-мера 88.71 % [23], русских, F-мера 86.1 % [19] и финских F-мера 86.82 % [24]. Для финского языка метод, основанный на правилах, даже превзошел популярную технологию BiLSTM-CRF, F-мера 84.59 %.…”

Section: классическая задача Nerunclassified

Name Entity Recognition Tasks: Technologies and Tools

Lagutina

Vasilyev

Zafievsky

2023

Model. anal. inf. sist.

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The task of named entity recognition (NER) is to identify and classify words and phrases denoting named entities, such as people, organizations, geographical names, dates, events, terms from subject areas. While searching for the best solution, researchers conduct a wide range of experiments with different technologies and input data. Comparison of the results of these experiments shows a significant discrepancy in the quality of NER and poses the problem of determining the conditions and limitations for the application of the used technologies, as well as finding new solutions. An important part in answering these questions is the systematization and analysis of current research and the publication of relevant reviews. In the field of named entity recognition, the authors of analytical articles primarily consider mathematical methods of identification and classification and do not pay attention to the specifics of the problem itself. In this survey, the field of named entity recognition is considered from the point of view of individual task categories. The authors identified five categories: the classical task of NER, NER subtasks, NER in social media, NER in domain, NER in natural language processing (NLP) tasks. For each category the authors discuss the quality of the solution, features of the methods, problems, and limitations. Information about current scientific works of each category is given in the form of a table for clarity. The review allows us to draw a number of conclusions. Deep learning methods are leading among state-of-the-art technologies. The main problems are the lack of datasets in open access, high requirements for computing resources, the lack of error analysis. A promising area of research in NER is the development of methods based on unsupervised techniques or rule-base learning. Intensively developing language models in existing NLP tools can serve as a possible basis for text preprocessing for NER methods. The article ends with a description and results of experiments with NER tools for Russian-language texts.

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“…Metrics Following previous work on Turkish NER (Yeniterzi, 2011;Şeker and Eryigit, 2012), we report the CoNLL F-1 metric (Tjong Kim Sang, 2002) to assess our NER baselines. CoNLL F-1 counts a named entity as correct, only if it is an exact match of the corresponding entity in the ground truth.…”

Section: Datasetsmentioning

confidence: 99%

Mukayese: Turkish NLP Strikes Back

Safaya¹,

Emirhan²,

Goktogan³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the stateof-the-art in NLP applications. As a solution, we present MUKAYESE, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking. All datasets and baselines are available under: https://github.com/ alisafaya/mukayese

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Turkish Named-Entity Recognition

Cited by 11 publications

References 17 publications

The effect of morphology in named entity recognition with sequence tagging

The effect of morphology in named entity recognition with sequence tagging

Name Entity Recognition Tasks: Technologies and Tools

Mukayese: Turkish NLP Strikes Back

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