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• W4386005200 abstract "Named Entity Recognition (NER) is a fundamental task for many applications in natural language processing (NLP). Previous deep learning models for NER task mainly used word embeddings to capture word similarity, but fail to encode orthographic features in entities. Extending word embedding with character-level information can encode such information without relying on external resources. However, research on character-level models is still in a fairly early stage in the task of NER: current work simply uses standard CNN or RNN to capture character information. The starting point of our paper is that certain subwords (character n-gram) are useful for NER. Inspired by the success of attention mechanism widely used in NLP, we propose an attention-based convolution neural network to build a character-aware word representation to allow our model to automatically learn such subwords. We evaluate our proposed model using two NER corpora and find that our model can achieve state-of-the-art performance without relying on external resources. By visualizing the attention layer, we find that typical subwords in entities can be learned effectively in the proposed model." @default.
• W4386005200 created "2023-08-20" @default.
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• W4386005200 date "2023-01-01" @default.
• W4386005200 modified "2023-10-12" @default.
• W4386005200 title "A Subword-Centric Character Level Representation for Named Entity Recognition" @default.
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• W4386005200 doi "https://doi.org/10.1007/978-3-031-37963-5_79" @default.
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