BERT-PersNER: a New Model for Persian Named Entity Recognition

Farahani, Farane Jalali; Ghassem-Sani, Gholamreza

doi:10.26615/978-954-452-072-4_073

Cited by 3 publications

(4 citation statements)

References 14 publications

(30 reference statements)

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“…The results are compared in in Table 6. Here, we compared our model to seven existing models, including traditional rule‐based models [19], deep neural network models with static embeddings [16,18,20,21], and state‐of‐the‐art transformer‐based models [22,23]. Note that the results of the compared models were obtained from the corresponding literature.…”

Section: Resultsmentioning

confidence: 99%

“…Note that the results of the compared models were obtained from the corresponding literature. As can be seen, the best results were obtained by the models proposed by Hafezi and Rezaeian [20] and Jalali Farahani and Ghassem‐Sani [23]. The model proposed by Hafezi and Rezaeian [20] is a BiLSTM‐CRF model with a pretrained character‐based vector, and the model proposed by Jalali Farahani and Ghassem‐Sani [23] is based on mBERT representations with a CRF layer on top.…”

Section: Resultsmentioning

confidence: 99%

“…They ranked second at the NSURL competition and achieved results showing that BERT is a favorable solution to tackle low‐resource problems based on transfer learning. Jalali Farahani and Ghassem‐Sani [23] also employed BERT for Persian NER, and they trained their model based on several active learning strategies.…”

Section: Related Workmentioning

confidence: 99%

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Comparative study of text representation and learning for Persian named entity recognition

Pour

Momtazi

2022

ETRI Journal

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Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.Named entity recognition (NER) detects and classifies named entities in text into categories, for example, persons, organizations, locations, and time expressions. NER is a fundamental task in many natural language processing (NLP) applications, for example, information extraction, text summarization, and machine translation, especially for low-resource languages, for example, Persian, which requires additional features to process the text effectively.NER is applied to various domains, and the set of entities is selected based on the given domain, for example, NER has been used to extract drug and disease mentions in English and Russian by Miftahutdinov and others [1]. In addition, Chen and others [2] investigated

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Comparative study of text representation and learning for Persian named entity recognition

Pour

Momtazi

2022

ETRI Journal

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“…Previous research has explored various techniques for developing NER systems (Dashtipour et al, 2017;Bokaei and Mahmoudi, 2018;Moradi et al, 2017;Ahmadi and Moradi, 2015;Balouchzahi and Shashirekha, 2021;Jalali Farahani and Ghassem-Sani, 2021;Team, 2021). To evaluate the quality of the generated datasets, we used the Hugging Face trainer (Wolf et al, 2020) and the xlmroberta-base (Conneau et al, 2019) model, which is a multilingual model capable of supporting both English and Persian.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English

Sartipi¹,

Fatemi²

2023

Preprint

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This study focuses on the generation of Persian named entity datasets through the application of machine translation on English datasets. The generated datasets were evaluated by experimenting with one monolingual and one multilingual transformer model. Notably, the CoNLL 2003 dataset has achieved the highest F1 score of 85.11%. In contrast, the WNUT 2017 dataset yielded the lowest F1 score of 40.02%. The results of this study highlight the potential of machine translation in creating high-quality named entity recognition datasets for low-resource languages like Persian. The study compares the performance of these generated datasets with English named entity recognition systems and provides insights into the effectiveness of machine translation for this task. Additionally, this approach could be used to augment data in low-resource language or create noisy data to make named entity systems more robust and improve them.

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Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English

Sartipi

Fatemi

2023

2023 9th International Conference on Web Research (ICWR)

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BERT-PersNER: a New Model for Persian Named Entity Recognition

Cited by 3 publications

References 14 publications

Comparative study of text representation and learning for Persian named entity recognition

Comparative study of text representation and learning for Persian named entity recognition

Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English

Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English

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