Handling Ambiguities in Sindhi Named Entity Recognition (SNER)

Nawaz, Dil; Awan, Shafique Ahmed; Bhutto, Zuhaibuddin; Memon, Mashooque Ahmed; Hameed, Mohd Abdul

doi:10.26692/surj/2017.09.08

Cited by 5 publications

(3 citation statements)

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“…The authors applied Bi-LSTM [85] based on DL which contains POS and CNN embedding character and overall accuracy has been calculated by using F1-score, recall and precision. Ali et al [2], [94] introduced a NER dataset for the limited resources Sindhi language with quality benchmarks with SiNER. It has 1,338 updates along with more than 1.35 million tokens that were gathered with the begin-inside-outside (BIO) tagging method used by Kawish and Awami Awaz Sindhi newspapers as the suggested dataset has a good potential of being a useful tool for statistical Sindhi language processing.…”

Section: (C) Deep Learning Approachmentioning

confidence: 99%

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

Pandey,

Nathani

2024

Preprint

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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.

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Section: (C) Deep Learning Approachmentioning

confidence: 99%

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

Pandey,

Nathani

2024

Preprint

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“…However, their work lacks the open-source implementation for further verification and extension. Afterwards, Nawaz et al [34] proposed a rule-based method by using an indexing approach to deal with the contextual ambiguities related to the Sindhi NER. However, their work lacks experimental results, which signify the usage of rules to deal with ambiguities for developing an automatic NER system.…”

Section: Sindhi Named Entity Recognitionmentioning

confidence: 99%

“…Their approach is also language-dependent and tested on a small number of tokens. Moreover, NER is a sequence labeling problem usually evaluated through classification metrics, such as precision, recall, and F1-score [35], but Nawaz et al [34] and Jumani et al [2] used accuracy metrics for assessing their rule-based models.…”

Section: Sindhi Named Entity Recognitionmentioning

confidence: 99%

Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition

Ali

Kumar

et al. 2021

Applied Sciences

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Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon contextual representation learning (CRL), bidirectional encoder, self-attention, and sequential conditional random field (CRF). The CaBiLSTM model incorporates task-oriented CRL based on joint character-level and word-level representations. It takes character-level input to learn the character representations. Afterwards, the character representations are transformed into word features, and the bidirectional encoder learns the word representations. The output of the final encoder is fed into the self-attention through a hidden layer before decoding. Finally, we employ the CRF for the prediction of label sequences. The baselines and the proposed CaBiLSTM model are compared by exploiting pretrained Sindhi GloVe (SdGloVe), Sindhi fastText (SdfastText), task-oriented, and CRL-based word representations on the recently proposed SiNER dataset. Our proposed CaBiLSTM model achieved a high F1-score of 91.25% on the SiNER dataset with CRL without relying on additional handmade features, such as hand-crafted rules, gazetteers, or dictionaries.

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