Document Theme Extraction Using Named-Entity Recognition

Nagrale, Deepali; Khatavkar, Vaibhav; Kulkarni, Parag

doi:10.1007/978-981-13-1513-8_52

Cited by 1 publication

(1 citation statement)

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“…The task of NER consisted in using SGML markers to identify entities in texts (names of persons, organizations, or places), temporal expressions, and numerical expressions ("currency" or "percentages"). Since then, NER has become a starting point and an important part of many applications in natural language processing (Ali et al, 2020), such as: Information Extraction (IE) (Kumar and Starly, 2022), Information Retrieval (IR) (Guo et al, 2009), Semantic Annotation (SA) , Machine Translation (MT) (Babych and Hartley, 2003), Question Answering (QA) systems (Yadav and Bethard, 2018), Text Summarization (Aone, 1999) and Text Clustering (Nagrale et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Arabic Pre-Trained Models on Named Entity Recognition Task

Hafedh Dahou,

Amine Cheragui,

Abdelali

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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Named Entity Recognition (NER) is a crucial task within natural language processing (NLP) that entails the identification and classification of entities such as person, organization and location. This study delves into NER specifically in the Arabic language, focusing on the Algerian dialect. While previous research in NER has primarily concentrated on Modern Standard Arabic (MSA), the advent of social media has prompted a need to address the variations found in different Arabic dialects. Moreover, given the notable achievements of Large-scale pre-trained models (PTMs) based on the BERT architecture, this paper aims to evaluate Arabic pre-trained models using an Algerian dataset that covers different domains and writing styles. Additionally, an error analysis is conducted to identify PTMs' limitations, and an investigation is carried out to assess the performance of trained MSA models on the Algerian dialect. The experimental results and subsequent analysis shed light on the complexities of NER in Arabic, offering valuable insights for future research endeavors.

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