Persian Automatic Text Summarization Based on Named Entity Recognition

Khademi, Mohammad Ebrahim; Fakhredanesh, Mohammad

doi:10.1007/s40998-020-00352-2

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…These models are often used in the fields of Information Retrieval System (IRS) Brandsen et al (2022), and Machine Translations (Xie et al, 2022). Question a n d answering Banerjee et al (2021) text summarization Khademi and Fakhredanesh (2020), etc.…”

Section: Related Workmentioning

confidence: 99%

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Padmanandam,

Sunitha,

Jafari

et al. 2024

Journal of Computer Science

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Named Entity Recognition (NER) is an information extraction task and one of the most researched applications to extract knowledge from massive data. Conventional NER systems identify predefined entities like name, person, location, organization, time, etc. However, there is a limitation to identifying user-defined entities that are specific to an application. This challenge introduces the concept of customized NER. For instance, if a learning management system like CourseNetworking (CN) needs to identify the skill set of a user from their posts, the existing pre-trained NER models cannot be used. To overcome this information extraction limitation, we propose a customized named entity recognition system for the CN platform using the deep learning model, Bi-Directional Encoder Representation from Transformer (BERT) which is a transformer-based deep learning technique where all output elements are connected to all input elements with dynamic weight connections. The proposed customization model can be employed to train any entity of user choice with a decent amount of training dataset. The model shows 70-72% recall and F1-Score varied on the number of epochs trained. This model is used in various applications like fraudulent detection, recommendation systems, and business intelligence.

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Section: Related Workmentioning

confidence: 99%

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Padmanandam,

Sunitha,

Jafari

et al. 2024

Journal of Computer Science

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show abstract

“…Named entity recognition is the process of locating and classifying named entities in text into predefined entity categories. In addition to being an independent tool for information extraction, NER plays an important role in various natural language processing applications, such as text understanding [12][13], information retrieval [14][15], text summarization [16], question answering systems [17], machine translation [18], etc. In recent years, due to its great success in various domains, deep learning has attracted widespread attention and research in the academic community.…”

Section: Related Workmentioning

confidence: 99%

Cross-domain NER in the data-poor scenarios for human mobility knowledge

Jiang,

Jin,

Chen

et al. 2024

Geoinformatica

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In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there's a need of Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in data-poor scenarios and achieve the best performance to date.

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Section: Related Workmentioning

confidence: 99%

Cross-domain NER in Data-poor Scenarios for Human Mobility Knowledge

Jiang

Jin

Chen

et al. 2023

Preprint

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In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there’s a need of Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive TextSequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility,the results show that this method can transfer task knowledge between multiple different domains in data-poor scenarios and achieve the best performance to date.

show abstract

Persian Automatic Text Summarization Based on Named Entity Recognition

Cited by 12 publications

References 27 publications

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Cross-domain NER in the data-poor scenarios for human mobility knowledge

Cross-domain NER in Data-poor Scenarios for Human Mobility Knowledge

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