A Survey About the Application of Artificial Intelligence in Search Engines

Ssemwogerere, Rajab; Sajo, Assadig Abdelrhman; Nambobi, Mutwalibi; Mzee, Asha Khamis

doi:10.4018/978-1-6684-6937-8.ch008

Cited by 1 publication

(1 citation statement)

References 56 publications

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“…Named Entity Recognition (NER) is one of the leading research areas in the NLP domain that extracts the named entities (NEs) from that data and classifies them into a predefined set of categories [1]. NER plays a crucial role in many NLP applications including question answering [2][3][4], query auto-completion systems [5][6][7], entity linking [8][9][10], and search engines [11][12][13] and the accurate classification of NEs depends significantly on extracting the NEs correctly. However, the complexities in extracting and classifying NEs vary across languages.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for Named Entity Recognition in Urdu language

Anam,

Anwar,

Jamal

et al. 2024

PLoS ONE

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Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies.

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