ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Jibril, Ebrahim Chekol; Tantuğ, A. Cüneyd

doi:10.1109/access.2023.3243468

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…Over the years, various approaches have been proposed for NER, including rule-based methods, statistical methods, and deep learning methods. In recent years, deep learning methods, particularly those based on Transformer models, have shown significant promise for NER use cases [7,8,9].…”

Section: Literature Reviewmentioning

confidence: 99%

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

2024

Journal of Electrical Systems

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Named Entity Recognition (NER) is a critical task in natural language processing that involves identifying and categorizing named entities in text. These entities can include people, organizations, locations, and more. Extracting features from these entities is equally important as it enables downstream tasks like sentiment analysis, text classification, and more. In this research paper, we propose an efficient Transformer-based model for identifying named entities and analyzing their features through ensemble operations. Our proposed model leverages the power of Transformer models such as BERT and XLNet for identifying named entities. We then convert the identified entities into feature vector sets using a combination of BERT and XLNet. These features are classified using the GoogLeNet convolutional neural network for model validation operations. By combining these different models through ensemble operations, we aim to improve the accuracy, precision, recall, and delay of the model for different use cases. The need for such a model arises due to the limitations of existing models for named entity recognition and feature analysis. While these models have achieved significant success, they still suffer from low accuracy, precision, recall, and high delay. Our proposed model overcomes these limitations by using ensemble operations to combine the strengths of different models. We compare the performance of our proposed model with existing models on standard datasets and show that it outperforms these models in terms of accuracy, precision, recall, and delay. Our results demonstrate the potential of ensemble operations in developing efficient models for named entity recognition and feature analysis. Overall, this research paper contributes to the development of more accurate and efficient models for natural language processing tasks.

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Section: Literature Reviewmentioning

confidence: 99%

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

2024

Journal of Electrical Systems

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“…Africa has a rich tradition of storytelling, poems, songs, and literature (Carter-Black, 2007;Banks-Wallace, 2002). Yet, it is only in recent years that there is nascent interest in NLP research for African languages, including Named Entity Recognition (NER; Adelani et al, 2021Adelani et al, , 2022cJibril and Tantug, 2023), Sentiment Analysis of Figurative Language in Twitter (Ghosh et al, 2015) Fine-grained Sentiment Analysis (Bethard et al, 2017) EmoContext: Contextual Emotion Detection in Text (Chatterjee et al, 2019) HaHackathon: Detecting and Rating Humor and Offense (Meaney et al, 2021) Aspect Based Sentiment Analysis (Pontiki et al, 2014) Sentiment Analysis in Twitter (Nakov et al, 2016) Affect in Tweets (Mohammad et al, 2018) Sentiment Analysis of Code-Mixed Tweets (Patwa et al, 2020) Structured Sentiment Analysis ( Barnes et al, 2022) Figure 2: A timeline of SemEval Shared Tasks from 2013 to 2022 with examples of sentiment analysis tasks.…”

Section: Introductionmentioning

confidence: 99%

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

Muhammad¹,

Abdulmumin²,

Yimam³

et al. 2023

Proceedings of the the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) 1 . AfriSenti-SemEval is a sentiment classification challenge in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorùbá) (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.

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Enhancing Sentiment Analysis in Amharic: Leveraging Transformer-Based Language Model for Low-Resource African Languages

Raychawdhary,

Das,

Bhattacharya

et al. 2024

SoutheastCon 2024

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ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Cited by 5 publications

References 56 publications

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

Enhancing Sentiment Analysis in Amharic: Leveraging Transformer-Based Language Model for Low-Resource African Languages

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