Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event trigger detection, which plays a key role in biomedical event extraction, has attracted significant attention recently. Hover, most approaches are based on statistical models, much relying on complex hand-designed features. ,  utilise the ability of Convolutional Neural Network CNN for addressing higher-level features automatically to explore correlations beten a trigger and an event type.  only keep one candidate trigger along with N-words around it and entity mention features as a raw input, giving up complex input with hand-designed features that derived from currently existed Natural Language Processing NLP tools. Our experiments on Multi-Level Event Extraction MLEE corpus shod that the method achieved a higher F-score of 78.67% compared to the state-of-the-art approaches. The results demonstrate that the proposed method is effective for event trigger detection.
['Jian Wang', 'Honglei Li', 'Yuan An', 'Hongfei Lin', 'Zhihao Yang']
I am writing a research paper on a idea Biomedical event trigger detection based on convolutional neural network
[{"from": "human", "value": "I am writing a research paper on a idea Biomedical event trigger detection based on convolutional neural network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event trigger detection, which plays a key role in biomedical event extraction, has attracted significant attention recently. Hover, most approaches are based on statistical models, much relying on complex hand-designed features. ,  utilise the ability of Convolutional Neural Network CNN for addressing higher-level features automatically to explore correlations beten a trigger and an event type.  only keep one candidate trigger along with N-words around it and entity mention features as a raw input, giving up complex input with hand-designed features that derived from currently existed Natural Language Processing NLP tools. Our experiments on Multi-Level Event Extraction MLEE corpus shod that the method achieved a higher F-score of 78.67% compared to the state-of-the-art approaches. The results demonstrate that the proposed method is effective for event trigger detection."}]