Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)
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| Challenge: | Named entity recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers . |
| Outcome: | The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark. |
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
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| Challenge: | Recent papers introduce methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. |
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| Challenge: | Named Entity Recognition (NER) is difficult in real-world settings due to short texts, emerging entities, and complex entities. |
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| Challenge: | Named entity recognition (NER) is a fundamental and important task in natural language processing. |
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| Challenge: | Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains. |
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