Abstract: Previous neural network models that perform named entity recognition (NER) typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases, or entities. Presented herein are novel approaches to learn coreference-aware word representations for the NER task. In one or more embodiments, a “CNN-BiLSTM-CRF” neural architecture is modified to include a coreference layer component on top of the BiLSTM layer to incorporate coreferential relations. Also, in one or more embodiments, a coreference regularization is added during training to ensure that the coreferential entities share similar representations and consistent predictions within the same coreference cluster. A model embodiment achieved new state-of-the-art performance when tested.

Abstract: Previous neural network models that perform named entity recognition (NER) typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases, or entities. Presented herein are novel approaches to learn coreference-aware word representations for the NER task. In one or more embodiments, a “CNN-BiLSTM-CRF” neural architecture is modified to include a coreference layer component on top of the BiLSTM layer to incorporate coreferential relations. Also, in one or more embodiments, a coreference regularization is added during training to ensure that the coreferential entities share similar representations and consistent predictions within the same coreference cluster. A model embodiment achieved new state-of-the-art performance when tested.