Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is an important step in most natural language processing (NLP) applications. |
| Approach: | They propose a dual-adversarial neural transfer method for addressing low-resource Named Entity Recognition (NER) they propose 'Generalized Resource-Adversarial Discriminator' and 'accidental training' |
| Outcome: | The proposed method improves on low-resource Named Entity Recognition (NER) with two variants, i.e., DATNet-F and DATNET-P, and adversarial training is adopted to boost model generalization. |
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| Challenge: | Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types. |
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| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
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| Challenge: | Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data . |
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