Abstract: Disclosed are a molecular detection system and a detection method thereof. The molecular detection system includes a main control device and a plurality of molecular detection devices. The main control device communicates with each of the plurality of molecular detection devices. The main control device is configured to control each of the plurality of molecular detection devices to perform detection. The main control device includes a display module configured to display detection data of each of the plurality of molecular detection devices. The molecular detection devices may perform different types of detections on different types of samples, which greatly expands the application flexibility and the application scenarios while meeting the detection throughput.

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.