Neural Joint Model for Part-of-Speech Tagging and Entity Extraction

Ali, Wazir; Kumar, Rajesh; Dai, Yong; Kumar, Jay; Tumrani, Saifullah

doi:10.1145/3457682.3457718

Cited by 3 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After comparison of these two models, it achieved a high F1-score of 91.25% by using CaBiLSTM on the SiNER dataset with CRL. Ali et al [84] suggested a neural joint model which is based on a bidirectional long-short memory (BiLSTM) [85] network and adversarial transfer learning to combine syntactic information from two tasks. The syntactic framework has been used to record and made available long-range connections between words.…”

Section: (C) Deep Learning Approachmentioning

confidence: 99%

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

View full text Add to dashboard Cite

Named Entity Recognition (NER) is a fundamental task of natural language processing (NLP) that focuses on the identification and classification of named entities such as name of individual persons, location, organization and dates within the text. NER plays a pivotal role in various NLP applications, including information extraction, question answering, text summarization and sentiment analysis. Natural language processing's (NLPs) fundamental issue is named entity recognition (NER). While extensive research has been conducted on NER for English and Hindi, the complexities of Indian languages present unique challenges that require customized solutions. Working with NER for Indian languages is a difficult endeavor with limited resources available. This article provides a comprehensive review of NER approaches tailored for Indian languages. Indian languages pose unique challenges to NER due to their rich morphological and syntactic variations, script diversity and limited annotated data availability. This paper reviews the various techniques and methodologies employed in NER for Indian languages, including rule-based, machine learning and deep learning approaches. It analyzes the strengths and limitations of each approach. Additionally, this article examines the recent advancements in transfer learning and multilingual models, showcasing their potential in improving NER performance across Indian languages. This paper aims to guide researchers and practitioners in the development of NER systems for Indian languages and foster further advancements in this field. This article also provides a comprehensive review of the diverse approaches employed for NER in Indian languages, highlighting the strength and limitations as well.

show abstract

Section: (C) Deep Learning Approachmentioning

confidence: 99%

State-of-art approach for Indian Language based on NER: Comprehensive Review

Pandey,

Nathani

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Sindhi is one of the most ancient languages in the world which has its own script in written and spoken forms [1][2][3]. Communication technologies are increasing day-by-day for different purposes, while different applications and software are used for daily communications such as WhatsApp, Facebook, Twitter, Telegram and Instagram [4][5]. In the community that uses Sindhi as their main language, Romanized Sindhi texts are used in daily communication especially in writing text messages on mobile phones, WhatsApp and other social media platforms [6].…”

Section: Introductionmentioning

confidence: 99%

Word by Word Labelling of Romanized Sindhi Text by using Online Python Tool

Sodhar¹,

Buller²,

Sulaiman³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Sindhi is one of the most ancient languages in the world and it has its own written and spoken scripts. After the rigorous study it was found that a lot of research work has been done in different languages, but word by word labelling of Sindhi language had not been done yet. In this research study, word labelling was done on 100 sentences of Romanized Sindhi texts using Python online tool. The dataset was collected from different sources which include Sindhi newspaper, blogs and social media webpages. From this dataset, a rule-based model has been applied for the Parts-of-Speech (POS) tagging of the Romanized Sindhi sentences. A total of 624 words of Romanized Sindhi texts were tested and successfully tagged by the SindhiNLP tool in which 482 words were tagged as nouns and pronouns, 92 words tagged as verbs and 50 words tagged as determinants.

show abstract