Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models (C18-1)
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| Challenge: | Existing models for Named Entity Recognition (NER) are trained on data with the same NE label set, but they are not able to recognize previously unseen NE categories. |
| Approach: | They propose to use a sequence to sequence model for Named Entity Recognition (NER) and propose to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs. |
| Outcome: | The proposed model can recognize previously unseen NE categories while keeping the knowledge of previously seen categories. |
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| Challenge: | Existing named entity recognition systems require large scale labeled data to perform, while annotation of NER data is laborious and time-consuming. |
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
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Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros
| Challenge: | Training a Named Entity Recognition model involves fixing a taxonomy of entity types . however, requirements evolve and a model may need to recognize additional entity types. |
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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: | Existing studies have shown that named entity recognition (NER) is effective in encoding and aggregating syntactic information, but they lack the appropriate knowledge to model such properties. |
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
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Transfer Learning for Entity Recognition of Novel Classes (C18-1)
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| Challenge: | Existing approaches to entity recognition are based on class labels in source and target domains, and many NER corpora only annotate a small number of categories. |
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