Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)
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| Challenge: | Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features. |
| Approach: | They propose to embed words and entity types into a low-dimensional vector space and compute a feature vector representing each word offline. |
| Outcome: | The proposed representations outperform existing models and achieve state-of-the-art performance. |
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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: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
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| Challenge: | a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with . |
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