Financial named entity recognition for Turkish news texts

Dinç, Duygu
Named Entity Recognition (NER) is a problem of information extraction where the objective is; in a given text, to detect and label named entities (NE) according to predetermined categories correctly. An NE may be a noun or a group of nouns which correspond to the name of a specific object, location or a concept in case of domain-specific applications. In the literature, person, organization, location names or date,time, money, percentage expressions are among highly studied, generic NEs. Besides, there are domain-specific studies with NEs that are related to specific do- mains like genetics, medicine, chemistry and finance. Solutions for NER problems may be useful in many downstream tasks in the Natural Language Processing do- main such as Text Summarization, Question Answering and Sentiment Analysis. For Turkish, which has pretty complex morphological features, there are less number of studies in NER field compared to more widely used languages like English. In recent years, neural-network based methods performed better in NER tasks than clas- sical rule-based or traditional machine learning techniques. In this thesis, most pop- ular deep-learning based models were experimented using different Turkish datasets. vMoreover, as being one of the focuses of this thesis, from raw financial news texts, two newly annotated datasets were presented and used throughout the experiments. New datasets were annotated using both BIO schema and raw labels, inter-annotator agreements were measured and models were trained separately using both versions to observe the effect of annotation format on performance. Moreover, new NEs specific to finance were also presented. Lastly, experiments with a few selected deep-learning based language-specific BERT models for some languages in Ural-Altaic language group were conducted.


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Citation Formats
D. Dinç, “Financial named entity recognition for Turkish news texts,” M.S. - Master of Science, Middle East Technical University, 2022.