Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Ullah, Fida | - |
dc.contributor.author | Ullah, Ihsan | - |
dc.contributor.author | Kolesnikova, Olga | - |
dc.date.accessioned | 2023-12-26T18:12:21Z | - |
dc.date.available | 2023-12-26T18:12:21Z | - |
dc.date.created | 2022-12-30 | - |
dc.date.issued | 2022-10-27 | - |
dc.identifier.isbn | 9783031194955 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/46788 | - |
dc.description.abstract | The named entity recognition (NER) task is a challenging problem in natural language processing (NLP), especially for languages with very few annotated corpora such as Urdu. In this paper we proposed an Attention-Bi-LSTM-CRF method and applied it to the MK-PUCIT Corpus which is the latest NER dataset available for the Urdu language. In addition to word-level embedding, we used an embedding-level focus mechanism. The output of the embedding layer was fed into a bidirectional-LSTM encoder unit, accompanied by another self-attention layer to boost the system’s accuracy. Our Attention-Bi-LSTM-CRF model demonstrated an F1-score of 92%. The cumulative findings of the experiments show that our approach outperforms existing methods, thus yielding a new UNER (Urdu Named Entity Recognition) state-of-the-art performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.language | English | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1007/978-3-031-19496-2_1 | - |
dc.identifier.scopusid | 2-s2.0-85142829581 | - |
dc.identifier.bibliographicCitation | 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, pp.3 - 17 | - |
dc.identifier.url | http://www.micai.org/2022/ | - |
dc.citation.conferencePlace | MX | - |
dc.citation.conferencePlace | Monterrey | - |
dc.citation.endPage | 17 | - |
dc.citation.startPage | 3 | - |
dc.citation.title | 21st Mexican International Conference on Artificial Intelligence, MICAI 2022 | - |
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